en: We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.

en: Capturing chemical intuition in synthesis of metal-organic frameworks

genetic-algorithms

display_name: genetic-algorithms

machine-learning

display_name: machine-learning

metal-organic-frameworks

display_name: metal-organic-frameworks

optimisation

display_name: optimisation

robotic-synthesi

display_name: robotic-synthesi

synthesis

display_name: synthesis


$oid: OBSOLETE

en: Biomolecular simulations are computationally expensive. This limits their application in drug or protein design and related fields. Several methods have been developed to address this problem. These methods often use an artificial force or potential acting on selected degrees of freedom known as collective variables. This requires explicit calculation of a collective variable (and its derivatives) from molecular structure. For collective variables that cannot be calculated explicitly or such calculations is slow we developed anncolvar package (https://github.com/spiwokv/anncolvar). This package approximates collective variables using artificial neural networks. It was tested on Isomap low dimensional representation of cyclooctane derivative or solvent-accessible surface area of Trp-cage miniprotein.

materialscloud

approval_status: approved

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

en: Approximation of Collective Variables by anncolvar

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

artificial-neural-networks

display_name: artificial-neural-networks

collective-variable

display_name: collective-variable

metadynamics

display_name: metadynamics


en:
Project Abstract: For applications of metal-organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation we develop a simple analytical model that predicts a material's Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework's intrinsic flexibility whereby performance is either improved or reduced with increasing flexibility, depending on the material's pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that the globally optimal separation exists within an entirely rigid pore. Molecular simulations show that our simple model predicts performance trends that are observed when screening the adsorption behavior of flexible MOFs. These flexible simulations provide better agreement with experimental adsorption data in a high performance material that is not captured when modeling this framework as rigid, an approximation typically made in high-throughput screening studies. We conclude that, for shape selective adsorption applications, the globally optimal material will have the optimal pore size/chemistry and minimal intrinsic flexibility even though other non-optimal materials' selectivity can actually be improved by flexibility. Equally important, we find that flexible simulations can be critical for correctly modeling adsorption in these types of systems.

About this entry: You can find the Xe/Kr Henry coefficients and the infinite dilution selectivity of more than 2000 CoRE MOF structures obtained via computational screening when materials are simulated as both flexible and rigid.  For details about the methods used to obtain these results, please see the corresponding paper (DOI: 10.1021/jacs.7b01688).  The data provided here was used to create the scatter plots presented in the original paper and can be used to find the adsorption properties for any material in the CoRE MOF screening.

Version 2 includes a minor formatting correction to make the CIF files compliant with the CIF standard.

en: The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials (Data Download)

flexibility

display_name: flexibility

nanoporous-materials

display_name: nanoporous-materials

xekr-separations

display_name: xekr-separations


en: Biomolecular simulations are computationally expensive. This limits their application in protein folding simulations, protein engineering, drug design and related fields. Enhanced sampling techniques such as metadynamics accelerates slow events in molecular simulation. This and other method apply artificial forces in directions of collective degrees of freedom (aka collective variables). Path collective variables and Property Map collective variables are defined using a series of reference structures of the studied molecular system. They require a huge number of mean square deviation calculations along the simulation. Close Structure algorithm reduces the number of these operations.

en: Reducing the Number of Mean-Square Deviation Calculations with Floating Close Structure in Metadynamics

mean-square-deviation

display_name: mean-square-deviation


en:
Project abstract: In this work, a theoretical framework is developed to explain and predict changes in the product distribution of the propene dimerization reaction, which yields a mixture of C6 olefin isomers, resulting from the use of different porous materials as catalysts. The MOF-74 class of materials has shown promise in catalyzing the dimerization of propene with high selectivity for valuable linear olefin products. We show that experimentally observed changes in the product distribution can be explained in terms of the contribution of the pores to the free energy of formation, which are directly computed using molecular simulation. Our model is used to screen a library of 118 existing and hypothetical MOF and zeolite structures to study how product distribution can be tuned by changing pore size, shape, and composition of porous materials. Using these molecular descriptors, catalyst properties are identified that increase the selective reaction of linear olefin isomers, which are valued as industrial feedstocks. A pore size commensurate with the size of the desired linear products enhances linear conversion by sterically hindering the branched isomers. Another promising feature is the presence of open metal sites, which interact with the olefin π-bond to provide favorable binding sites for the linear isomers.

About this entry: We provide the geometrical framework descriptors, linear conversions, and thermodynamic data for the 118 frameworks screened in this study, as well as thermodynamics of adsorption properties computed for 12 C6 olefin isomers in each framework. For details about the methods used to obtain these data, please see the corresponding paper (DOI: 10.1021/acscatal.7b00712). The data provided here was used to create the scatter plots in the original paper and can be used to find the product distribution and adsorption properties for all of the materials considered in the study.

en: Predicting Product Distribution of Propene Dimerization in Nanoporous Materials (Data Download)

metalorganic-frameworks

display_name: metalorganic-frameworks

molecular-simulation

display_name: molecular-simulation

monte-carlo

display_name: monte-carlo

product-distribution

display_name: product-distribution

propene-dimerization

display_name: propene-dimerization

zeolites

display_name: zeolites


en:
Charge equilibration (Qeq) methods can estimate the electrostatic potential of molecules and periodic frameworks by assigning point charges to each atom, using only a small fraction of the resources needed to compute density functional (DFT)-derived charges. This makes possible, for example, the computational screening of thousands of microporous structures to assess their performance for the adsorption of polar molecules. Recently, different variants of the original Qeq scheme were proposed to improve the quality of the computed point charges. One focus of this research was to improve the gas adsorption predictions in Metal Organic Frameworks (MOFs), for which many different structures are available. In this work, we review the evolution of the method from the original Qeq scheme, understanding the role of the different modifications on the final output. We evaluated the result of combining different protocols and set of parameters, by comparing the Qeq charges with high quality DFT-derived DDEC charges for 2338 MOF structures. We focused on the systematic errors that are attributable to specific atom types to quantify the final precision that one can expect from Qeq methods in the context of gas adsorption where the electrostatic potential plays a significant role, namely CO2 and H2S adsorption. In conclusion, both the type of algorithm and the input parameters have a large impact on the resulting charges and we draw some guidelines to help the user to choose the proper combination of the two for obtaining a meaningful set of charges. We show that, considering this set of MOFs, the accuracy of the original Qeq scheme is often still comparable with the most recent variants, even if it clearly fails in the presence of certain atom types, such as alkali metals.

Version 2 of the record adds the data underlying the figures in CSV format.

en: Evaluating charge equilibration methods to generate electrostatic fields in nanoporous materials

charge-equilibration-methods

display_name: charge-equilibration-methods

marvel

display_name: marvel

partial-charges

display_name: partial-charges

qeq

display_name: qeq


en: Project Abstract: Pore volume is one of the main properties for the characterization of microporous crystals. It is experimentally measurable and it can also be obtained from the refined unit cell by a number of computational techniques. In this work we assess the accuracy and the discrepancies between the different computational methods which are commonly used for this purpose, i.e, geometric, helium and probe center pore volume, by studying a database of more than 5000 frameworks. We developed a new technique to fully characterize the internal void of a microporous material and to compute the probe accessible and occupiable pore volume. We show that unlike the other definitions of pore volume, the occupiable pore volume can be directly related to the experimentally measured pore volumes from nitrogen isotherms.

en: Accurate Characterization of the Pore Volume in Microporous Crystalline Materials (Data Download)

pore-volume

display_name: pore-volume

void-fraction

display_name: void-fraction


en: Zeolite-templated carbons (ZTCs) comprise a relatively recent material class synthesized via the chemical vapor deposition of a carbon-containing precursor on a zeolite template, followed by the removal of the template. We have developed a theoretical framework to generate a ZTC model from any given zeolite structure, which we show can successfully predict the structure of known ZTCs. We use our method to generate a library of ZTCs from all known zeolites, to establish criteria for which zeolites can produce experimentally accessible ZTCs, and to identify over 10 ZTCs that have never before been synthesized. We show that ZTCs partition space into two disjoint labyrinths that can be described by a pair of interpenetrating nets. Since such a pair of nets also describes a triply periodic minimal surface (TPMS), our results establish the relationship between ZTCs and schwarzites—carbon materials with negative Gaussian curvature that resemble TPMSs—linking the research topics and demonstrating that schwarzites should no longer be thought of as purely hypothetical materials.

en: Generating carbon schwarzites via zeolite-templating

carbon-materials

display_name: carbon-materials

schwarzites

display_name: schwarzites

tpms

display_name: tpms

ztc

display_name: ztc


en: The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C-C cross-coupling reaction. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18,062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$/mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include earth abundant (Cu) transition metal with less common ligands. Our results indicate that modern statistical learning techniques can be applied to the computational discovery of readily available and promising catalyst candidates.

en: Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

homogeneous-catalysis

display_name: homogeneous-catalysis

machine-learning

display_name: machine-learning

transition-metal-complexes

display_name: transition-metal-complexes

volcano-plot

display_name: volcano-plot


en: Canonical molecular dynamics simulations of crystal growth from solution suffer from severe finite-size effects. As the crystal grows, the solute molecules are drawn from the solution to the crystal, leading to a continuous drop in the solution concentration. This is in contrast to experiments in which the crystal grows at an approximately constant supersaturation of a bulk solution. Recently, Perego et al. [J. Chem. Phys.2015, 142, 144113] showed that in a periodic setup in which the crystal is represented as a slab, the concentration in the vicinity of the two surfaces can be kept constant while the molecules are drawn from a part of the solution that acts as a molecular reservoir. This method is quite effective in studying crystallization under controlled supersaturation conditions. However, once the reservoir is depleted, the constant supersaturation conditions cannot be maintained. We propose a variant of this method to tackle this depletion problem by simultaneously dissolving one side of the crystal while letting the other side grow. A continuous supply of particles to the solution due to the crystal dissolution maintains a steady solution concentration and avoids reservoir depletion. In this way, a constant supersaturation condition can be maintained for as long as necessary. We have applied this method to study the growth and dissolution of urea crystal from water solution under constant supersaturation and undersaturation conditions, respectively. The computed growth and dissolution rates are in good agreement with those obtained in previous studies.

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: Materials Cloud

en: A Cannibalistic Approach to Grand Canonical Crystal Growth

crystal-nucleation-and-growth

display_name: crystal-nucleation-and-growth

erc

display_name: erc

solution-crystallization

display_name: solution-crystallization


en:
Classical turning surfaces of Kohn-Sham potentials separate classically-allowed regions (CARs) from classically-forbidden regions (CFRs). They are useful for understanding many chemical properties of molecules, but need not exist in solids, where the density never decays to zero. At equilibrium geometries, we find that CFRs are absent in perfect metals, rare in covalent semiconductors at equilibrium, but common in ionic and molecular crystals. In all materials, CFRs appear or grow as the internuclear distances are uniformly expanded. They can also appear at a monovacancy in a metal. Calculations with several approximate density functionals and codes confirm these behaviors. A classical picture of conduction suggests that CARs should be connected in metals, and disconnected in wide-gap insulators, and is confirmed in the limits of extreme compression and expansion. Surprisingly, many semiconductors have no CFR at equilibrium, a key finding for density functional construction. Nonetheless, a strong correlation with insulating behavior can still be inferred. Moreover, equilibrium bond lengths for all cases can be estimated from the bond type and the sum of the classical turning radii of the free atoms or ions.

This record contains machine readable data for this work

en: Calculation and interpretation of classical turning surfaces in solids

envi

en: Environment

density-functional-theory

display_name: density-functional-theory

dft

display_name: dft

kohn-sham-potential

display_name: kohn-sham-potential

turning-surface

display_name: turning-surface


en:
We provide the input files to reproduce the data presented in the work:
Hidden Beneath the Surface: Origin of the Observed Enantioselective Adsorption on PdGa(111)
The files are subdivided in directories named after the figures/table of the manuscript
A. V. Yakutovich, J. Hoja, D. Passerone, Alexandre Tkatchenko, C. A. Pignedoli
J. Am. Chem. Soc., 140, 1401-1408 (2018) DOI: 10.1021/jacs.7b10980
In the work, we unravel the origin of the recently observed striking enantioselectivity of the PdGa(111) surface with respect to the adsorption of a small organic molecule, 9-ethynylphenanthrene, using first-principles calculations. It turns out that the key ingredient to understand the experimental evidence is the appropriate description of van der Waals interactions beyond the widely employed atomic pairwise approximation. A recently developed van der Waals-inclusive density functional method, which encompasses dielectric screening effects, reveals the origin of the experimentally observed enantioselectivity and provides conclusive evidence of chiral recognition on a bimetallic surface driven by dispersion interactions.

chirality

display_name: chirality

many-body-dispersion

display_name: many-body-dispersion

marvel

display_name: marvel

pdga

display_name: pdga


en: The study of organic molecular crystals under high pressure provides fundamental insight into crystal packing distortions and reveals mechanisms of phase transitions and the crystallization of polymorphs. These solid state transformations can be monitored directly by analyzing electron charge densities that are experimentally obtained at high pressure. However, restricting the analysis to the featureless electron density does not reveal the chemical bonding nature and the existence of intermolecular interactions. This shortcoming can be resolved by the use of the DORI (Density Overlap Region Indicator) descriptor, which is capable of detecting simultaneously both covalent patterns and non-covalent interactions from electron density and its derivatives. Using the biscarbonyl[14]annulene crystal under pressure as an example, we demonstrate how DORI can be exploited on experimental electron densities to reveal and monitor changes in electronic structure patterns resulting from molecular compression. A novel approach based on a flood fill type algorithm is proposed for analyzing the topology of the DORI isosurface. This approach avoids the arbitrarily selection of DORI isovalues and provides an intuitive way to assess how compression packing affects covalent bonding in organic solids.

en: DORI reveals the influence of non-covalent interactions on covalent bonding patterns in molecular crystals under pressure

high-pressure

display_name: high-pressure

molecular-scalar-fields

display_name: molecular-scalar-fields

organic-crystals

display_name: organic-crystals

topology

display_name: topology


en: Ice nucleation is a process of great relevance in physics, chemistry, technology, and environmental sciences; much theoretical effort has been devoted to its understanding, but it still remains a topic of intense research. We shed light on this phenomenon by performing atomistic based simulations. Using metadynamics and a carefully designed set of collective variables, reversible transitions between water and ice are able to be simulated. We find that water freezes into a stacking disordered structure with the all-atom transferable intermolecular potential with 4 points/ice (TIP4P/ice) model, and the features of the critical nucleus of nucleation at the microscopic level are revealed. We have also estimated the ice nucleation rates along with other nucleation parameters at different undercoolings. Our results are in agreement with recent experimental and other theoretical works, and they confirm that nucleation is preceded by a large increase in tetrahedrally coordinated water molecules.

en: Temperature Dependence of Homogeneous Nucleation in Ice

enhanced-sampling

display_name: enhanced-sampling

homegenous-nucleation

display_name: homegenous-nucleation

ice

display_name: ice

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics

plumed

display_name: plumed


en: We describe a double helical conformation in the densely charged aromatic polyamide poly(2,2'- disulfonyl-4,4'-benzidine terephthalamide) or PBDT. This double helix macromolecule represents one of the most rigid simple molecular structures known, exhibiting an extremely high axial persistence length (~ 1 micrometer).

en: Double helix PBDT polymer - Submitted manuscript, simulations and other source data

ionic

display_name: ionic

nematic

display_name: nematic

nmr

display_name: nmr

polymer

display_name: polymer

simulations

display_name: simulations

x-ray

display_name: x-ray


en:
Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations and proton disorder. This is made possible by combining advanced free energy methods and state-of-the-art machine learning techniques. The ab initio description leads to structural properties in excellent agreement with experiments, and reliable estimates of the melting points of light and heavy water. We observe that nuclear quantum effects contribute a crucial 0.2 meV/H2O to the stability of ice Ih, making it more stable than ice Ic. Our computational approach is general and transferable, providing a comprehensive framework for quantitative predictions of ab initio thermodynamic properties using machine learning potentials as an intermediate step.

In this set of supplemental materials, we have included the neural network potential for bulk water, including its training set in two different formats. We have also included the input files for running free energy calculations.

en: Ab initio thermodynamics of liquid and solid water: supplemental materials

free-energy-calculation-input-files

display_name: free-energy-calculation-input-files

machine-learning-potential

display_name: machine-learning-potential

machine-learning-potential-training-set

display_name: machine-learning-potential-training-set


en: We present a large dataset of adsorption of H, C, N, O and S onto more than 2,000 metallic and bimetallic alloy surfaces, consisting of approximately 90,000 DFT calculations performed in Quantum Espresso. The alloys are constructed from all possible combinations of 37 metals into AB and A3B stoichiometries, in the L1_0 and L1_2 structures respectively, where the 37 metals in the A1 structure are included as well. Slabs are cleaved along the 111 facet for A1 and L1_2 and along the 101 facet for L1_0, and all possible adsorption sites are sampled. In addition to the monoatomic adsorbates, adsorption of CH, CH2, CH3, NH, NH2, OH, H2O and SH  is sampled for a smaller subset of alloys.

en: High-throughput calculations of catalytic properties of bimetallic alloy surfaces

chemisorption

display_name: chemisorption

dft

display_name: dft

heterogeneous-catalysis

display_name: heterogeneous-catalysis

surfaces

display_name: surfaces


dopant-discovery-in-cuprous-iodide

en: Computational data to support an extensive study of prospective dopants in the zinc blende (gamma) phase of cuprous iodide. This phase of CuI holds the current record hole conductivity for intrinsic transparent p-type semiconductors. In the corresponding scientific article, a high-throughput approach was employed to systematically explore strategies for enhancing CuI further by impurity incorporation. Our objectives were to identify a practical approach for increasing hole conductivity in CuI thin films and to explore the possibility for ambivalent doping. A total of 64 chemical elements was investigated. This materials cloud record contains all optimized defective structures for Cu and I substitutional sites, calculation settings and some of the output (LOCPOT files, required for electrostatic correction calculations are not included due to their large size).  Chalcogen elements were found to display acceptor character when substituting iodine. Further eight impurities suitable for n-type doping were also discovered. Unfortunately, our work further revealed that donor doping is hindered by compensating native defects.

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: Dopant discovery in cuprous iodide

title_for_slug: dopant-discovery-in-cuprous-iodide

cui

display_name: cui

defects

display_name: defects

dopants

display_name: dopants

supercell

display_name: supercell

transparent-conductors

display_name: transparent-conductors


en: This is a LAMMPS readable EAM type potential file for Hf-Nb-Ta-Zr based high-entropy alloys (HEAs). In this potential file the elements are sequenced as Hf, Nb, Ta and Zr, respectively. This potential was previously used to model the HEA for the local lattice distortions due to short-range order clustering found in the alloy after long-term annealing at high-temperature. The input to build this potential is based on the physical properties of pure elements such as the lattice parameter, cohesive energy, elastic constants etc and the details are discussed in the associated reference. Units used are in eV/atom for energy and Angstrom for distance.

materialscloud

en: Materials Cloud

en: EAM potential for Hf-Nb-Ta-Zr high-entropy alloy

eam-potential

display_name: eam-potential

high-entropy-alloys

display_name: high-entropy-alloys

lammps

display_name: lammps

refractory-elements

display_name: refractory-elements


en: Zethrenes are model diradicaloids with potential applications in spintronics and optoelectronics. Despite a rich chemistry in solution, on-surface synthesis of zethrenes has never been demonstrated. This record contains data to support our recent publication where we report the on-surface synthesis of super-heptazethrene on Au(111). Scanning tunneling spectroscopy investigations reveal that super-heptazethrene exhibits an exceedingly low HOMO–LUMO gap of 230 meV and, in contrast to its open-shell singlet ground state in the solution phase and in the solid-state, likely adopts a closed-shell ground state on Au(111).

en: On-surface synthesis of super-heptazethrene

envi

en: Environment

ab-initio

display_name: ab-initio

cscs

display_name: cscs

diradicaloid

display_name: diradicaloid

erc

display_name: erc

on-surface-synthesis

display_name: on-surface-synthesis

snsf

display_name: snsf


en: Two-dimensional transition metal dichalcogenides (TMDs) of Mo and W in their 1T′ crystalline phase host the quantum spin Hall (QSH) insulator phase. We address the electronic properties of the QSH edge states by means of first-principles calculations performed on realistic models of edge terminations of different stoichiometries. The QSH edge states show a tendency to have complex band dispersions and coexist with topologically trivial edge states. We nevertheless identify two stable edge terminations that allow isolation of a pair of helical edge states within the band gap of TMDs, with monolayer 1T′-WSe2 being the most promising material. We also characterize the finite-size effects in the electronic structure of 1T′-WSe2 nanoribbons. Our results provide guidance to the experimental studies and possible practical applications of QSH edge states in monolayer 1T′-TMDs.

en: Controlling the quantum spin Hall edge states in two-dimensional transition metal dichalcogenides

edge-states

display_name: edge-states

epfl

display_name: epfl

marvel

display_name: marvel

quantum-spin-hall-effect

display_name: quantum-spin-hall-effect

topological-insulators

display_name: topological-insulators

transition-metal-dichalcogenides

display_name: transition-metal-dichalcogenides


en: We propose a general method to calculate the average misfit volumes of atoms in any random alloy via DFT calculations. The method is validated with an example of a 6-component equi-composition high entropy alloy. The special quasi-random structures (SQSs) used in our work are reported here.

en: Special quasi-random structures for the 6-component high entropy alloys

misfit-volumes

display_name: misfit-volumes

random-alloys

display_name: random-alloys

special-quasi-random-structures-sqss

display_name: special-quasi-random-structures-sqss


en: The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a bulk material are written as a sum over contributions that depend on the configurations within finite atom-centered environments. The obvious downside of this approach is that it cannot capture nonlocal, nonadditive effects such as those arising due to long-range electrostatics or quantum interference. We propose a solution to this problem by introducing nonlocal representations of the system, which are remapped as feature vectors that are defined locally and are equivariant in O(3). We consider, in particular, one form that has the same asymptotic behavior as the electrostatic potential. We demonstrate that this framework can capture nonlocal, long-range physics by building a model for the electrostatic energy of randomly distributed point-charges, for the unrelaxed binding curves of charged organic molecular dimers, and for the electronic dielectric response of liquid water. By combining a representation of the system that is sensitive to long-range correlations with the transferability of an atom-centered additive model, this method outperforms current state-of-the-art machine-learning schemes and provides a conceptual framework to incorporate nonlocal physics into atomistic machine learning.

en: Incorporating long-range physics in atomic-scale machine learning

long-range-interactions

display_name: long-range-interactions

machine-learning

display_name: machine-learning


en: We investigate formation energies of C, Si, and Ge defects in β-Ga2O3 through hybrid functional calculations. We find that the interstitial defects of these elements generally occur at higher energies than their substitutional counterparts, while they are more stable at low Fermi energies in Ga-rich conditions. In n-type and Ga-rich conditions, interstitials of Si and Ge show significantly higher formation energies than their substitutional form, but this difference is less pronounced for C. Charge transition levels of interstitial defects lie in the upper part of the band-gap, and account for several measured levels in unintentionally doped and Ge-doped samples of β-Ga2O3.

en: Defect Formation Energies of Interstitial C, Si, and Ge Impurities in β-Ga2O3

defects

display_name: defects

hybrid-functional

display_name: hybrid-functional


en: Maximally-localised Wannier functions (MLWFs) are routinely used to compute from first-principles advanced materials properties that require very dense Brillouin zone integration and to build accurate tight-binding models for scale-bridging simulations. At the same time, high-throughput (HT) computational materials design is an emergent field that promises to accelerate the reliable and cost-effective design and optimisation of new materials with target properties. The use of MLWFs in HT workflows has been hampered by the fact that generating MLWFs automatically and robustly without any user intervention and for arbitrary materials is, in general, very challenging. We address this problem directly by proposing a procedure for automatically generating MLWFs for HT frameworks. Our approach is based on the selected columns of the density matrix method (SCDM) and we present the details of its implementation in an AiiDA workflow. We apply our approach to a dataset of 200 bulk crystalline materials that span a wide structural and chemical space. We assess the quality of our MLWFs in terms of the accuracy of the band-structure interpolation that they provide as compared to the band-structure obtained via full first-principles calculations. We provide here an AiiDA export file with the full provenance of all simulations run in the project. Moreover, we provide a downloadable virtual machine that allows to reproduce the results of this paper and also to run new calculations for different materials, including all first-principles and atomistic simulations and the computational workflows.

aiida-workflow

display_name: aiida-workflow

automated-wannierisation

display_name: automated-wannierisation

band-structure-interpolation

display_name: band-structure-interpolation

high-throughput-simulations

display_name: high-throughput-simulations

marvelosp

display_name: marvelosp

maximally-localised-wannier-functions

display_name: maximally-localised-wannier-functions

scdm

display_name: scdm


en: Nonbenzenoid carbocyclic rings are postulated to serve as important structural elements toward tuning the chemical and electronic properties of extended polycyclic aromatic hydrocarbons (PAHs, or namely nanographenes), necessitating a rational and atomically precise synthetic approach toward their fabrication. This record contains data supporting a recent work where, using a combined bottom-up in-solution and on-surface synthetic approach, we report the synthesis of nonbenzenoid open-shell nanographenes containing two pairs of embedded pentagonal and heptagonal rings.

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: Open-shell Non-benzenoid Nanographenes Containing Two Pairs of Pentagonal and Heptagonal Rings

ab-initio

display_name: ab-initio

nanographene

display_name: nanographene

on-surface-synthesis

display_name: on-surface-synthesis

polycyclic-aromatic-hydrocarbons

display_name: polycyclic-aromatic-hydrocarbons

scanning-tunneling-microscopy

display_name: scanning-tunneling-microscopy


en: Oxygen vacancies are a common source of excess electrons in complex oxides. In Mott insulators, these additional electrons can induce a metal-insulator transition (MIT), fundamentally altering the electronic properties of the  system. Here we study the effect of oxygen vacancies in LaTiO3, a prototypical Mott insulator close to the MIT. We show that the introduction of oxygen vacancies creates a vacancy-related band immediately below the partially filled Ti-t 2g bands. We study the effect of this additional band on the Mott MIT  using a combination of density functional theory and dynamical mean-field theory (DFT+DMFT), employing a minimal correlated subspace consisting of effective Ti-t 2g orbitals plus an additional Wannier function centered on the vacancy site. We find that the Mott insulating state in LaTiO3 is robust to the presence of the vacancy band, which remains fully occupied even in the presence of a local Coulomb repulsion, and therefore does not cause a doping of the Mott insulator

materialscloud

en: Materials Cloud

en: DFT+DMFT study of oxygen vacancies in a Mott insulator

dftdmft

display_name: dftdmft

electronic-structure

display_name: electronic-structure

mott-insulators

display_name: mott-insulators

oxygen-vacancies

display_name: oxygen-vacancies


en: We investigate the synergy occurring in photocatalytic water splitting between the metal-organic framework MIL-125-NH2 and two co-catalysts, namely NiO and Ni2P, by calculating their band edge alignment with respect to the redox levels of liquid water. For the NiO/H2O and Ni2P/H2O interfaces, we employ an explicit atomistic description of water and perform molecular dynamics simulations considering both molecular and dissociated water adsorbed at the co-catalyst surface. For the MIL-125-NH2/NiO and MIL-125-NH2/Ni2P interfaces, we rely on the concept of charge neutrality and use a scheme combining the electron affinities and the charge neutrality levels of the interface components. We provide a description of the underlying fundamental processes that is consistent with photoluminescence and intrinsic activity experiments and that supports NiO and Ni2P as suitable co-catalysts for MIL-125-NH2 as far as the hydrogen evolution reaction is concerned.

en: Unraveling the synergy between metal-organic frameworks and co-catalysts in photocatalytic water splitting

co-catalyst

display_name: co-catalyst

density-functional-calculations

display_name: density-functional-calculations

first-principles-calculations

display_name: first-principles-calculations

hydrogen-evolution

display_name: hydrogen-evolution

metal-organic-frameworks

display_name: metal-organic-frameworks

molecular-dynamics

display_name: molecular-dynamics

photocatalysis

display_name: photocatalysis

water-splitting

display_name: water-splitting


en: Singlet Fission (SF) has demonstrated significant promise for boosting the power conversion efficiency (PCE) of solar cells. Traditionally, SF is targeted as an intermolecular process, however its dependence on crystal packing makes molecular design difficult. In contrast, intramolecular SF (iSF) enables the exploration of tunable bi-chromophoric systems following well-defined structure-property relationships. In this work, we propose a set of parameters to screen conjugated donor-acceptor copolymer candidates with potential iSF behaviour. We focus our analysis on the E(S1)>2E(T1) thermodynamic condition and on the appropriate charge transfer (CT) character of S1. We map the CT character with respect to the frontier molecular orbital (FMO) energies of the constituent monomers, providing a cost-effective protocol for an accelerated screening of promising iSF donor-acceptor pairs, while minimizing the number of computations. These parameters are applied to a chemically diverse, curated library of 81 truncated dimers of synthetically feasible donor-acceptor copolymers. From our dataset, four candidates are flagged for iSF, two of which were previously experimentally reported. This protocol is envisioned to be scaled up for the high-throughput screening of large databases of donor-acceptor dimers for the design and identification of conjugated polymers capable of iSF.

en: Designing Singlet Fission Candidates from Donor-Acceptor Copolymers

envi

en: Environment

da-copolymers

display_name: da-copolymers

dft-computations

display_name: dft-computations

epfl

display_name: epfl

screening-descriptors

display_name: screening-descriptors

singlet-fission

display_name: singlet-fission


en: We investigate the solvation effect of water on the overpotentials of the oxygen evolution reaction on rutile TiO2 by applying the thermodynamic integration method on atomistic model interfaces with and without the water molecules. We compare the results at the vacuum interface with the commonly used computational hydrogen electrode method, finding overall good agreement. The effect of the solvent is found to be twofold. First, the explicit treatment of the solvent can lead to equilibrium configurations differing from the relaxed structures without solvent. Second, the overpotentials can be affected by up to 0.5 eV. The energetics are subject to electrostatic effects at the interface rather than to modifications in the hydrogen bond network. These results provide a promising perspective for treating the solvent with implicit models.

en: Effect of the Solvent on the Oxygen Evolution Reaction at the TiO2-Water Interface

explicit-solvent

display_name: explicit-solvent

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

rutile

display_name: rutile

solvation-effect

display_name: solvation-effect

water

display_name: water


en: We describe a nonempirical procedure for achieving accurate band gaps of extended systems through the insertion of suitably defined potential probes. By enforcing Koopmans' condition on the resulting localized electronic states, we determine the optimal fraction of Fock exchange to be used in the adopted hybrid functional. As potential probes, we consider point defects, the hydrogen interstitial, and various adjustable potentials that allow us to vary the energy level of the localized state in the band gap. By monitoring the delocalized screening charge, we achieve a measure of the degree of hybridization with the band states, which can be used to improve the band-gap estimate. Application of this methodology to AlP, C, and MgO yields band gaps differing by less than 0.2 eV from experiment.

en: Adjustable potential probes for band-gap predictions of extended systems through nonempirical hybrid functionals

band-gap

display_name: band-gap

first-principles

display_name: first-principles

hybrid-functional

display_name: hybrid-functional

point-defect

display_name: point-defect

potential-probe

display_name: potential-probe


en: In this work, we investigate the angle-dependent magnetoresistance (AMR) of the layered nodal-line Dirac semimetal ZrSiS for the in-plane and out-of-plane current directions. Combining the Fermi surfaces calculated from first principles with the Boltzmann's semiclassical transport theory, we reproduce all the prominent features of the unusual behavior of the in-plane and out-of-plane AMR.We can conclude that the dominant contribution the cusplike AMR lies in open orbits of the hole pocket and, in general, AMR is strongly influenced by charge compensation effect and the off-diagonal conductivity tensor elements, which give rise to peculiar butterfly-shaped AMR.

en: Highly anisotropic interlayer magnetoresitance in ZrSiS nodal-line Dirac semimetal

boltzmann-transport-theory

display_name: boltzmann-transport-theory

fermi-surface

display_name: fermi-surface

magnetoresistance

display_name: magnetoresistance

marvel

display_name: marvel

wanniertools

display_name: wanniertools


en: Two-dimensional materials are emerging as a promising platform for ultrathin channels in field-effect transistors. To this aim, novel high-mobility semiconductors need to be found or engineered. Although extrinsic mechanisms can in general be minimized by improving fabrication processes, the suppression of intrinsic scattering (driven, for example, by electron–phonon interactions) requires modification of the electronic or vibrational properties of the material. Because intervalley scattering critically affects mobilities, a powerful approach to enhance transport performance relies on engineering the valley structure. We show here the power of this strategy using uniaxial strain to lift degeneracies and suppress scattering into entire valleys, dramatically improving performance. This is shown in detail for arsenene, where a 2% strain stops scattering into four of the six valleys and leads to a 600% increase in mobility. The mechanism is general and can be applied to many other materials, including in particular the isostructural antimonene and blue phosphorene. In this entry we provide the AiiDA database with the calculation of the electron-phonon matrix elements for arsenene, both in the equilibrium and in the strained case, together with scripts to retrieve and plot them.

en: Valley-Engineering Mobilities in Two-Dimensional Materials

electronphonon-scattering

display_name: electronphonon-scattering

intervalley

display_name: intervalley

transport

display_name: transport


en: Using a density-functional framework, we investigate the vibrational spectra of vitreous SiO2 to determine to what extent these spectra provide information about the medium-range structure of the oxide network. We carry out a comparative study involving three model structures, which all feature a nondefective network of corner-sharing tetrahedra but differ through their Si-O-Si bond-angle distributions and ring statistics. We first address the results of typical diffraction probes. Fair agreement with experiment is achieved for the total neutron and total x-ray structure factors of all models, indicating limited sensitivity of these structure factors to the medium-range structure. The same consideration also applies to the Si-O and O-O partial structure factors. At variance, the Si-Si partial structure factor is found to be highly sensitive to the Si-O-Si bond-angle distribution. We then address typical vibrational spectra, such as the inelastic neutron spectrum, the infrared spectra, and the Raman spectra. Our study indicates that the considered experimental data are globally consistent with a medium-range structure characterized by an average Si-O-Si bond angle of 148° and with small-ring concentrations as derived from the intensities of the experimental Raman defect lines. To describe the infrared and Raman couplings, our work also introduces parametric models which reproduce well the spectra calculated from first principles.

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: Medium-range structure of vitreous SiO2

cscs

display_name: cscs

dft

display_name: dft

epfl

display_name: epfl

infrared

display_name: infrared

raman

display_name: raman

snsf

display_name: snsf


en: The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost. Applications are shown for various hydrocarbon molecules of increasing complexity and flexibility, and demonstrate the accuracy of the model when predicting the density on octane and octatetraene after training exclusively on butane and butadiene. This transferable, data-driven model can be used to interpret experiments, accelerate electronic structure calculations, and compute electrostatic interactions in molecules and condensed-phase systems.

materialscloud

en: Materials Cloud

en: Transferable Machine-Learning Model of the Electron Density

electron-density

display_name: electron-density

machine-learning

display_name: machine-learning


en: Distinct to type-I Weyl semimetals (WSMs) that host quasiparticles described by the Weyl equation, the energy dispersion of quasiparticles in type-II WSMs violates Lorentz invariance and the Weyl cones in the momentum space are tilted. Since it was proposed that type-II Weyl fermions could emerge from WTe2, MoTe2, WP2 and MoP2 families of materials, a large number of experiments have been dedicated to unveiling the possible manifestation of type-II WSMs, e.g., surface-state Fermi arcs. However, the interpretations of the experimental results are very controversial. Here, using angle-resolved photoemission spectroscopy supported by the first-principles calculations, we probe the tilted Weyl cone bands in the bulk electronic structure of WP2 directly, which are at the origin of Fermi arcs at the surfaces and transport properties related to the chiral anomaly in type-II WSMs. Our results ascertain that, due to the spin-orbit coupling, the Weyl nodes originate from the splitting of fourfold degenerate band-crossing points with Chern numbers C=2 or  C=-2, which is unique among all the discovered WSMs. Our finding also provides a guiding line to observe the chiral anomaly that could manifest in novel transport properties. Input files for theoretical calculations and raw data from ARPES measurements are included in this record.

en: Observation of Weyl Nodes in Robust Type-II Weyl Semimetal WP2

arpes

display_name: arpes

type-ii-weyl-semimetal

display_name: type-ii-weyl-semimetal

vasp

display_name: vasp


en:
From the Ising model and the Lennard-Jones fluid to water and the iron-carbon system, phase diagrams are an indispensable tool to understand phase equilibria. Despite the effort of the simulation community, the calculation of a large portion of a phase diagram using computer simulation is still today a significant challenge. Here, we propose a method to calculate phase diagrams involving liquid and solid phases by the reversible transformation of the liquid and the solid. To this end, we introduce an order parameter that breaks the rotational symmetry and we leverage our recently introduced method to sample the multithermal-multibaric ensemble. In this way, in a single molecular dynamics simulation, we are able to compute the liquid-solid coexistence line for entire regions of the temperature and pressure phase diagram. We apply our approach to the bcc-liquid phase diagram of sodium and the fcc-bcc-liquid phase diagram of aluminum.

This repository contains the input files to reproduce the results of the paper mentioned above. The results are divided into two zip files Al.zip and Na.zip for simulations of aluminum and sodium, respectively. Inside each folder there is an example for the calculation of free energies at constant temperature and pressure, and the input files and results of the multithermal-multibaric simulations.

en: Calculation of phase diagrams in the multithermal-multibaric ensemble

envi

en: Environment

aluminum

display_name: aluminum

enhanced-sampling

display_name: enhanced-sampling

free-energy

display_name: free-energy

multithermal-multibaric

display_name: multithermal-multibaric

phase-diagrams

display_name: phase-diagrams

sodium

display_name: sodium

ves

display_name: ves


en:
In a recent work, we demonstrated the bottom-up on-surface synthesis of poly(para-dibenzo[bc,kl]-coronenylene) , a zigzag edge-encased analog of poly(para-phenylene), and its lateral fusion into zigzag edge-extended graphene nanoribbons.
The record contains data to reproduce the calculations that were performed at Empa to support the findings discussed in the manuscript.

en: Graphene Nanoribbons Derived From Zigzag Edge-Encased Poly(para-2,9-dibenzo[bc,kl]coronenylene) Polymer Chains

ab-initio

display_name: ab-initio

garphene-nanoribbons

display_name: garphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis


en: We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whether the structural diversity found in this database can be well-represented by classical descriptors, such as distances, angles, and ring sizes, or whether a more general representation of the atomic structure, furnished by the smooth overlap of atomic position (SOAP) method, is required to capture accurately structure–property relations. We assessed the quality of each descriptor by machine-learning the molar energy and volume for each hypothetical framework in the dataset. We have found that a SOAP representation with a cutoff length of 6 Å, which goes beyond near-neighbor tetrahedra, best describes the structural diversity in the Deem database by capturing relevant interatomic correlations. Kernel principal component analysis shows that SOAP maintains its superior performance even when reducing its dimensionality to those of the classical descriptors and that the first three kernel principal components capture the main variability in the dataset, allowing a 3D point cloud visualization of local environments in the Deem database. This "cloud atlas" of local environments was found to show good correlations with the contribution of a given motif to the density and stability of its parent framework. Local volume and energy maps constructed from the SOAP/machine learning analyses provide new images of zeolites that reveal smooth variations of local volumes and energies across a given framework and correlations between the contributions to volume and energy associated with each atom-centered environment.

en: A New Kind of Atlas of Zeolite Building Blocks

erc

display_name: erc

marvel

display_name: marvel

sccer

display_name: sccer

zeolites

display_name: zeolites


en: This record mainly upload all the raw experimental data related to the manuscript. Fig1 include the transport data, magnetotransport data, AHE data, ESR data, uSR data. These data related to the conclusion that spin fluctuation established in the paramagnetic phase. Fig2 contains in-plane and out-of-plane 3D photoemission spectra, and high resolution cuts on top and side surface. Fig3 contains band electronic structure along high symmetry lines with spin along all kinds of typical directions. Fig4 contains 3-D in-plane and out-of-plane Fermi surfaces on the 101 cleavage, high resolution cuts, STM/STS data. The proved the observation of Weyl points.

en: Spin fluctuation induced Weyl semimetal state in the paramagnetic phase of EuCd2As2

arpes

display_name: arpes

magnetic-material

display_name: magnetic-material

magnetic-weyl-semimetal

display_name: magnetic-weyl-semimetal

spin-fluctuation

display_name: spin-fluctuation

topological-material

display_name: topological-material


en: This database includes Isotropic lattice thermal conductivities of MgSiO3 post-perovskite under the lowermost mantle conditions. The conductivity was calculated based on the ab initio anharmonic lattice dynamics with solving the linearized phonon Boltzmann transport equation.

en: Lattice thermal conductivity of MgSiO3 post-perovskite under the lowermost mantle conditions from ab initio anharmonic lattice dynamics

density-functional-theory

display_name: density-functional-theory

earths-lower-mantle-mineral

display_name: earths-lower-mantle-mineral

phonon-transport-properties

display_name: phonon-transport-properties


en: Coronoids, polycyclic aromatic hydrocarbons with geometrically defined cavities, are promising model structures of porous graphene. This record contains data to support our recent research where we report the on-surface synthesis of C168 and C140 coronoids, referred to as [6]- and [5]coronoid, respectively, using 5,9-dibromo-14-phenylbenzo[m]tetraphene as the precursor. These coronoids entail large cavities (>1 nm) with inner zigzag edges, distinct from their outer armchair edges. While [6]coronoid is planar, [5]coronoid is not. Low-temperature scanning tunneling microscopy/spectroscopy and noncontact atomic force microscopy unveil structural and electronic properties in accordance with those obtained from density functional theory calculations. Detailed analysis of ring current effects identifies the rings with the highest aromaticity of these coronoids, whose pattern matches their Clar structure. The pores of the obtained coronoids offer intriguing possibilities of further functionalization toward advanced host–guest applications.

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: Large-cavity coronoids with different inner and outer edge structures

aromaticity

display_name: aromaticity

coronoid

display_name: coronoid

cscs

display_name: cscs

snsf

display_name: snsf


en: This entry provides the snapshots of liquid water generated with ab initio molecular dynamics using rVV10 density functional at room temperature. Nuclear quantum effects are taken into account through path-integral molecular dynamics simulations.

materialscloud

en: Materials Cloud

en: Ab initio electronic structure of liquid water: Molecular dynamics snapshots

liquid-water

display_name: liquid-water

nuclear-quantum-effect

display_name: nuclear-quantum-effect


en: Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by covalent, hydrogen or van der Waals interactions - self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extremely important, though very challenging, and so far, relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning. Such methods, however, do not guarantee that all possible patterns will be considered and often rely on intuition. Here we introduce a much simpler, though rigorous, hierarchical geometric model founded on the mean-field theory of 2D polygonal tessellations to predict extended network patterns based on molecular-level information. Based on graph theory, this approach yields pattern classification and pattern prediction within well-defined ranges. When applied to existing experimental data, our model provides an entirely new view of self-assembled molecular patterns, leading to interesting predictions on admissible patterns and potential additional phases. While developed for hydrogen-bonded systems, an extension to covalently bonded graphene-derived materials or 3D structures such as fullerenes is possible, significantly opening the range of potential future applications.

en: Polygonal tessellations as predictive models of molecular monolayers

molecular-self-assembly

display_name: molecular-self-assembly

tesselation

display_name: tesselation


en: Several enhanced sampling methods, such as umbrella sampling or metadynamics, rely on the identification of an appropriate set of collective variables. Recently two methods have been proposed to alleviate the task of determining efficient collective variables. One is based on linear discriminant analysis; the other is based on a variational approach to conformational dynamics and uses time-lagged independent component analysis. In this paper, we compare the performance of these two approaches in the study of the homogeneous crystallization of two simple metals. We focus on Na and Al and search for the most efficient collective variables that can be expressed as a linear combination of X-ray diffraction peak intensities. We find that the performances of the two methods are very similar. Wherever the different metastable states are well-separated, the method based on linear discriminant analysis, based on its harmonic version, is to be preferred because simpler to implement and less computationally demanding. The variational approach, however, has the potential to discover the existence of different metastable states.

en: Improving collective variables: The case of crystallization

envi

en: Environment

crystalization

display_name: crystalization

enhanced-sampling

display_name: enhanced-sampling

hlda

display_name: hlda

marvel

display_name: marvel

plumed

display_name: plumed


en: Rare-earth nickelates exhibit a metal–insulator transition accompanied by a structural distortion that breaks the symmetry between formerly equivalent Ni sites. The quantitative theoretical description of this coupled electronic–structural instability is extremely challenging. Here, we address this issue by simultaneously taking into account both structural and electronic degrees of freedom using a charge self-consistent combination of density functional theory and dynamical mean-field theory, together with screened interaction parameters obtained from the constrained random phase approximation. Our total energy calculations show that the coupling to an electronic instability toward a charge disproportionated insulating state is crucial to stabilize the structural distortion, leading to a clear first order character of the coupled transition. The decreasing octahedral rotations across the series suppress this electronic instability and simultaneously increase the screening of the effective Coulomb interaction, thus weakening the correlation effects responsible for the metal–insulator transition. Our approach allows to obtain accurate values for the structural distortion and thus facilitates a comprehensive understanding, both qualitatively and quantitatively, of the complex interplay between structural properties and electronic correlation effects across the nickelate series.

en: Energetics of the coupled electronic–structural transition in the rare-earth nickelates

dynamical-mean-field-theory

display_name: dynamical-mean-field-theory

electronic-properties-and-materials

display_name: electronic-properties-and-materials

quantum-materials

display_name: quantum-materials


en: We present all AMONs for GDB and Zinc data-bases using no more than 7 non-hydrogen atoms (AGZ7)---a calculated organic chemistry building-block dictionary based on the AMON approach [Huang and von Lilienfeld, Nature Chemistry (2020)]. AGZ7 records Cartesian coordinates of compositional and constitutional isomers, as well as properties for ∼140k small organic molecules obtained by systematically fragmenting all molecules of Zinc and the majority of GDB17 into smaller entities, saturating with hydrogens, and containing no more than 7 heavy atoms (excluding hydrogen atoms). AGZ7 cover the elements H, B, C, N, O, F, Si, P, S, Cl, Br, Sn and I and includes optimized geometries, total energy and its decomposition, Mulliken atomic charges, dipole moment vectors, quadrupole tensors, electronic spatial extent, eigenvalues of all occupied orbitals, LUMO, gap, isotropic polarizability, harmonic frequencies, reduced masses, force constants, IR intensity, normal coordinates, rotational constants, zero-point energy, internal energy, enthalpy, entropy, free energy, and heat capacity (all at ambient conditions) using B3LYP/cc-pVTZ (pseudopotentials were used for Sn and I) level of theory. We exemplify the usefulness of this data set with AMON based machine learning models of total potential energy predictions of seven of the most rigid GDB-17 molecules.

en: Dictionary of 140k GDB and ZINC derived AMONs

building-blocks

display_name: building-blocks

erc

display_name: erc

organic-chemical-space

display_name: organic-chemical-space

quantum-machine-learning

display_name: quantum-machine-learning


en: Interatomic potentials are often necessary to describe complex realistic systems that would be too costly to study from first-principles. Commonly, interatomic potentials are designed using functional forms driven by physical intuition and fitted to experimental or computational data. The moderate flexibility of these functional forms limits their ability to be systematically improved by increasing the fitting datasets; on the other hand, their qualitative description of the essential physical interactions ensures a modicum degree of transferability. Recently, a novel trend has emerged where potential-energy surfaces are represented by neural networks fitted on large numbers of first-principles calculations, thus maximizing flexibility but requiring extensive datasets to ensure transferability. Gaussian Approximation Potentials in particular are a novel class of potentials based on non-linear, non-parametric Gaussian-process regression. Here we generate a Gaussian Approximation model for the α-phase of iron training on energies, stresses and forces taken from first-principles molecular dynamics simulations of pristine and defected bulk systems, of surfaces and γ-surfaces with different crystallographic orientations.

en: Gaussian Approximation Potentials for iron from extended first-principles database (Data Download)

artificial-neural-networks

display_name: artificial-neural-networks

gaussian-approximation-potentials

display_name: gaussian-approximation-potentials

interatomic-potentials

display_name: interatomic-potentials

iron

display_name: iron

machine-learning

display_name: machine-learning


en: Methods based on the second order Møller–Plesset perturbation theory (MP2) and the Random Phase Approximation (RPA) have emerged as practicable and reliable approaches to improve the accuracy of density functional approximations for first principle atomistic simulations. Such approaches are in fact capable to account ab-initio for non-local dynamical electron correlation effects, which play a fundamental role, for example, in the description of non-bonded interactions. To assess the performance of MP2 and RPA for real applications, isobaric-isothermal Monte Carlo simulations have been performed to study the structural properties of bulk liquid water under ambient conditions. The choice of bulk liquid water as benchmark system is motivated by the complicated nature of the intermolecular interactions, where repulsion, polarization, hydrogen bonding and van der Waals forces play an important role and are particularly difficult to reproduce accurately in atomistic models. The results demonstrate the feasibility of such approaches which open the way for further applications.

en: Isobaric-Isothermal Monte Carlo Simulations of Bulk Liquid Water from MP2 and RPA Theory (MC Trajectories Data Download)

bulk-liquid-water

display_name: bulk-liquid-water

isobaric-isothermal-npt

display_name: isobaric-isothermal-npt

monte-carlo-simulations

display_name: monte-carlo-simulations

rpa

display_name: rpa


en:
With the growth of natural gas as an energy source, upgrading CO2-contaminated supplies has become increasingly important. Here we develop a single metric that captures how well an adsorbent performs the separation of CH4 and CO2, and we then use this metric to computationally screen tens of thousands of all-silica zeolites. We show that the most important predictors of separation performance are the CO2 heat of adsorption (Qst, CO2) and the CO2 saturation loading capacity. We find that a higher-performing material results when the absolute value of the CH4 heat of adsorption (Qst, CH4) is decreased independently of Qst, CO2, but a correlation that exists between Qst, CH4 and Qst, CO2 in all-silica zeolites leads to incongruity between the objectives of optimizing Qst, CO2 and minimizing Qst, CH4, rendering Qst, CH4 nonpredictive of separation performance. We also conduct a large-scale analysis of ideal adsorbed solution theory (IAST) by comparing results obtained using directly-generated mixture isotherms to those obtained using IAST; IAST appears adequate for the purposes of establishing performance trends and structure–property relationships in a high-throughput manner, but it must be tested for validity when analyzing individual adsorbents in detail since it can produce significant errors for materials in which there is site segregation of the adsorbate species.

Version 2 provides the structures in CIF format.

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: High-throughput computational screening of nanoporous adsorbents for CO 2 capture from natural gas

database

display_name: database

dc

display_name: dc

deliverable-capacities

display_name: deliverable-capacities

gcmc

display_name: gcmc

grand-canonical-monte-carlo

display_name: grand-canonical-monte-carlo

high-throughput

display_name: high-throughput

iza

display_name: iza

methane-storage

display_name: methane-storage

nanoporous

display_name: nanoporous

porous-polymer-networks

display_name: porous-polymer-networks

three-dimensional

display_name: three-dimensional

zeolites

display_name: zeolites


enhanced-sampling-of-transition-states

en: The free energy landscapes of several fundamental processes are characterized by high barriers separating long-lived metastable states. In order to explore these type of landscapes enhanced sampling methods are used. While many such methods are able to obtain sufficient sampling in order to draw the free energy, the transition states are often sparsely sampled. We propose an approach based on the Variationally Enhanced Sampling Method to enhance sampling in the transition region. To this effect, we introduce a dynamic target distribution which uses the derivative of the instantaneous free energy surface to locate the transition regions on the fly and modulate the probability of sampling different regions. Finally, we exemplify  the effectiveness of this approach in enriching the number of configurations in the transition state region in the cases of a chemical reaction and of a nucleation process.

materialscloud

en: Materials Cloud

en: Enhanced sampling of transition states

title_for_slug: enhanced-sampling-of-transition-states

critical-clusters

display_name: critical-clusters

enhanced-sampling

display_name: enhanced-sampling

molecular-modelling

display_name: molecular-modelling

nucleation

display_name: nucleation

transition-states

display_name: transition-states


en:
Metal–organic frameworks (MOFs), a class of porous materials, are of particular interest in gas storage and separation applications due largely to their high internal surface areas and tunable structures. MOF-5 is perhaps the archetypal MOF; in particular, many isoreticular analogues of MOF-5 have been synthesized, comprising alternative dicarboxylic acid ligands. In this contribution we introduce a new set of hypothesized MOF-5 analogues, constructed from commercially available organic molecules. We describe our automated procedure for hypothetical MOF design, comprising selection of appropriate ligands, construction of 3D structure models, and structure relaxation methods. 116 MOF-5 analogues were designed and characterized in terms of geometric properties and simulated methane uptake at conditions relevant to vehicular storage applications. A strength of the presented approach is that all of the hypothesized MOFs are designed to be synthesizable utilizing ligands purchasable online.

Version 2 includes the structures in CIF format.

en: Mail-order metal-organic frameworks (MOFs): designing isoreticular MOF-5 analogues comprising commercially available organic molecules

database

display_name: database


en: Dipole polarizabilities (and other molecular properties) computed using linear response coupled cluster theory (LR-CCSD/d-aug-cc-pVDZ) and hybrid density functional theory (B3LYP/d-aug-cc-pVDZ, SCAN0/d-aug-cc-pVDZ, and B3LYP/d-aug-cc-pVTZ) for the 7,211 molecules in the QM7b database and the 52 molecules in the AlphaML showcase database.

en: Quantum Mechanical Static Dipole Polarizabilities in the QM7b and AlphaML Showcase Databases

envi

en: Environment

density-functional-theory

display_name: density-functional-theory

dipole-polarizability

display_name: dipole-polarizability

linear-response-coupled-cluster

display_name: linear-response-coupled-cluster


en: Data for the case study of TuTraSt on methane diffusion in zeolites, using a standard kinetic Monte Carlo simulation based on the output of our grid analysis. We find that it is accurate, fast, and rigorous without limitations to the geometries of the diffusion tunnels or transition states.

en: TuTraST data for methane diffusion in IZA structures

diffusion

display_name: diffusion

kinetic-monte-carlo

display_name: kinetic-monte-carlo

marvel

display_name: marvel

methane

display_name: methane

transition-state-theory

display_name: transition-state-theory

tutrast

display_name: tutrast

zeolite

display_name: zeolite


en:
Despite the enormous success and popularity of density functional theory, systematic verification and validation studies are still very limited both in number and scope. Here, we propose a universal standard protocol to verify publicly available pseudopotential libraries, based on several independent criteria including verification against all-electron equations of state and plane-wave convergence tests for phonon frequencies, band structure, cohesive energy and pressure. Adopting these criteria we obtain two optimal pseudopotential sets, namely the Standard Solid State Pseudopotential (SSSP) efficiency and precision libraries, tailored for high-throughput materials screening and high-precision materials modelling. As of today, the SSSP precision library is the most accurate open-source pseudopotential library available. This archive entry contains the database of calculations (phonons, cohesive energy, equation of state, band structure, pressure, etc.) together with the provenance of all data and calculations as stored by AiiDA.

*** Recent UPDATE April 2023 - Version 1.3.0 ***
The pseudopotentials of elements At, Fr, Ra are added from PSlibrary. The pseudopotential of actinides are added from dataset of https://www.uni-marburg.de/de/fb15/arbeitsgruppen/anorganische_chemie/ag-kraus/forschung/paw_datasets_for_the_actinoids

Please refer to the CHANGELOG.txt file within the archive to view a complete list of version changes.

en: A Standard Solid State Pseudopotentials (SSSP) library optimized for precision and efficiency

aiida

display_name: aiida

calculations

display_name: calculations

chessboards

display_name: chessboards

cohesive-energy

display_name: cohesive-energy

cscs

display_name: cscs

dft

display_name: dft

efficiency

display_name: efficiency

electronic-bands

display_name: electronic-bands

electronic-properties

display_name: electronic-properties

elemental-solids

display_name: elemental-solids

high-throughput

display_name: high-throughput

library

display_name: library

max

display_name: max

phonons

display_name: phonons

prace

display_name: prace


en: The record contains data that support the work where we report the optical imaging and absorption spectroscopy on atomically precise armchair graphene nanoribbons (GNRs) on insulating fused silica substrates. This is achieved by controlling light polarization on macroscopically aligned GNRs which greatly enhances the optical contrast of the submonolayer GNRs on the insulating substrates. We measure the linear absorption spectra of 7-armchair and 9-armchair GNRs in this study, and the experimental data agree qualitatively with ab inito calculation results. The polarization spectroscopy technique enables an unambiguous optical identification of GNRs and provides a rapid tool to characterize the transferred film over a large area.

en: Optical imaging and spectroscopy of atomically precise armchair graphene nanoribbons

absorption-spectroscopy

display_name: absorption-spectroscopy

graphene-nanoribbons

display_name: graphene-nanoribbons

optical-imaging

display_name: optical-imaging

snsf

display_name: snsf


en: The production of hydrogen fuels, via water splitting, is of practical relevance for meeting global energy needs and mitigating the environmental consequences of fossil-fuel-based transportation. Water photoelectrolysis has been proposed as a viable approach for generating hydrogen, provided that stable and inexpensive photocatalysts with conversion efficiencies over 10% can be discovered, synthesized at scale, and successfully deployed (Pinaud et al., Energy Environ. Sci., 2013, 6, 1983). While a number of first-principles studies have focused on the data-driven discovery of photocatalysts, in the absence of systematic experimental validation, the success rate of these predictions may be limited. We address this problem by developing a screening procedure with co-validation between experiment and theory to expedite the synthesis, characterization, and testing of the computationally predicted, most desirable materials. Starting with 70150 compounds in the Materials Project database, the proposed protocol yielded 71 candidate photocatalysts, 11 of which were synthesized as single-phase materials. Experiments confirmed hydrogen generation and favorable band alignment for 6 of the 11 compounds, with the most promising ones belonging to the families of alkali and alkaline-earth indates and orthoplumbates. This study shows the accuracy of a nonempirical, Hubbard-corrected density-functional theory method to predict band gaps and band offsets at a fraction of the computational cost of hybrid functionals, and outlines an effective strategy to identify photocatalysts for solar hydrogen generation.

en: Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen

density-functional-theory

display_name: density-functional-theory

dftu

display_name: dftu

high-throughput-screening

display_name: high-throughput-screening

marvel

display_name: marvel

photocatalysis

display_name: photocatalysis

water-splitting

display_name: water-splitting


en: Optical materials play a key role in enabling modern optoelectronic technologies in a wide variety of domains such as the medical or the energy sector. Among them, nonlinear optical crystals are of primary importance to achieve a broader range of electromagnetic waves in the devices. However, numerous and contradicting requirements significantly limit the discovery of new potential candidates, which, in turn, hinders the technological development. In the present work, the static nonlinear susceptibility and dielectric tensor are computed via density functional perturbation theory for a set of 579 inorganic semiconductors. The aim of this work is to provide a relevant dataset to foster the identification of promising nonlinear optical crystals in order to motivate their subsequent experimental investigation.

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: Second-harmonic generation tensors from high-throughput density-functional perturbation theory

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

first-principles

display_name: first-principles

non-linear-optics

display_name: non-linear-optics

second-harmonic-generation

display_name: second-harmonic-generation


en:
We present a computational screening of experimental structural repositories for fast Li-ion conductors, with the goal of finding new candidate materials for application as solid-state electrolytes in next-generation batteries. We start from ~1400 unique Li-containing materials, of which ~900 are insulators at the level of density-functional theory. For those, we calculate the diffusion coefficient in a highly automated fashion, using extensive molecular dynamics simulations on a potential energy surface (the recently published pinball model) fitted on first-principles forces. The ~130 most promising candidates are studied with full first-principles molecular dynamics, first at high temperature and then more extensively for the 78 most promising candidates. The results of the first-principles simulations of the candidate solid-state electrolytes found are discussed in detail.

Update April 2024:
Files are added that facilitate the Materials Cloud Archive OPTIMADE service to serve the structural data of this Archive entry via an OPTIMADE API. The molecular dynamics trajectories are served as individual structures per time-step.

materialscloud

en: Materials Cloud

en: High-throughput computational screening for solid-state Li-ion conductors

big-map

display_name: big-map

computational-high-throughput-screening

display_name: computational-high-throughput-screening

first-principles-molecular-dynamics

display_name: first-principles-molecular-dynamics

li-ion-conductors

display_name: li-ion-conductors

solid-state-electrolytes

display_name: solid-state-electrolytes


en: We study the oxo-hexametallate Li7TaO6 with first-principles and classical molecular dynamics simulations, obtaining a low activation barrier for diffusion of ∼0.29 eV and a high ionic conductivity of 5.7×10−4 S cm−1 at room temperature (300 K). We find evidence for a wide electrochemical stability window from both calculations and experiments, suggesting its viable use as a solid-state electrolyte in next-generation solid-state Li-ion batteries. To assess its applicability in an electrochemical energy storage system, we performed electrochemical impedance spectroscopy measurements on multicrystalline pellets, finding substantial ionic conductivity, if below the values predicted from simulation. We further elucidate the relationship between synthesis conditions and the observed ionic conductivity using X-ray diffraction, inductively coupled plasma optical emission spectrometry, and X-ray photoelectron spectroscopy, and study the effects of Zr and Mo doping.

en: The solid-state Li-ion conductor Li7TaO6: A combined computational and experimental study

electrochemical-stability

display_name: electrochemical-stability

li-ion-batteries

display_name: li-ion-batteries

molecular-dynamics

display_name: molecular-dynamics

polarizable-force-fields

display_name: polarizable-force-fields

solid-state-electrolyte

display_name: solid-state-electrolyte


en: We perform a thorough structural search with the minima hopping method (MHM) to explore low-energy structures of methylammonium lead iodide. By combining the MHM with a forcefield, we efficiently screen vast portions of the configurational space with large simulation cells containing up to 96 atoms. Our search reveals two structures of methylammonium iodide perovskite (MAPI) that are substantially lower in energy than the well-studied experimentally observed low-temperature orthorhombic phase. The data set containing approximately ~180,000 crystal structures is provided.

en: Emergence of hidden phases of methylammonium lead-iodide (CH3NH3PbI) upon compression

envi

en: Environment

force-field-gulp

display_name: force-field-gulp

mapi-perovskites

display_name: mapi-perovskites

minima-hopping-method

display_name: minima-hopping-method

structure-prediction

display_name: structure-prediction

vasp-scan--functional

display_name: vasp-scan--functional


en:
This dataset provides DFT (density functional theory as implemented in VASP, Vienna Ab Initio Simulation Package) calculations for pure Magnesium. It was designed by Binglun Yin, Markus Stricker and William A. Curtin for fitting a neural network potential with Behler-Parrinello symmetry functions. Binglun Yin carried out the calculation.

It corresponds to a dataset that is commonly used to fit interatomic potentials for mechanics applications and includes structure-energy relationships for structures used to calculate:
1. Bulk properties
2. Generalized stacking fault energies
3. Decohesion and relaxed surfaces
4. Dimer
5. Corner and rod geometries
6. Vacancy formation energy

en: Pure Magnesium DFT calculations for interatomic potential fitting

decohesion

display_name: decohesion

dft

display_name: dft

elasticity

display_name: elasticity

epfl

display_name: epfl

magnesium

display_name: magnesium

metallurgy

display_name: metallurgy

snsf

display_name: snsf

stacking-faults

display_name: stacking-faults

surfaces

display_name: surfaces


en: Generalized stacking fault energy (GSFE) is a crucial material property for describing nanoscale plasticity in crystalline materials, such as dislocation dissociation, nucleation, and twinning. The dependence of the GSFE on applied stress normal to the stacking fault (SF) plane has been suggested to influence such phenomena. Here, the SF stress dependence is analyzed through (i) the generalized stacking fault potential energy (GSFE) and (ii) the generalized stacking fault enthalpy (GSFH). Our DFT calculations reveal that the GSFE is almost independent of the applied normal stress, which contradicts the long-standing wisdom and previous studies. We also reveal the inelastic inter-planar normal displacement associated with the SF. The coupling between the positive inelastic normal displacement and the applied normal stress decreases the GSFH.

en: Stress-dependence of generalized stacking fault energies: a DFT study

erc

display_name: erc

generalized-stacking-fault-energy

display_name: generalized-stacking-fault-energy

stacking-fault-stress-dependence

display_name: stacking-fault-stress-dependence


en: The data contained in this record, raw data of images and input files to reproduce calculations, support our recent report for the on-surface synthesis and characterization of two ultralow-gap open-shell molecules, namely peri-tetracene, a benzenoid graphene fragment with zigzag edge topology, and dibenzo[a,m]dicyclohepta[bcde,nopq]rubicene, a nonbenzenoid nonalternant structural isomer of peri-tetracene with two embedded azulene units. Our results provide an understanding of the ramifications of altered bond topologies at the single-molecule scale, with the prospect of designing functionalities in carbon-based nanostructures via engineering of bond topology

en: Tailoring Bond Topologies in Open-Shell Graphene Nanostructures

atom-manipulation

display_name: atom-manipulation

density-functional-theory

display_name: density-functional-theory

scanning-tunneling-microscopy

display_name: scanning-tunneling-microscopy

scanning-tunneling-spectroscopy

display_name: scanning-tunneling-spectroscopy


en:
This data set contains atomic structures of liquid water and amorphous Li(x)Si in the XCrySDen structure format (XSF) [1]. Total energies are included as additional meta information. The extended XSF format is compatible with the Atomic Energy Network (ænet) package [2,3] for artificial neural network (ANN) potential construction and application.

Liquid water: The liquid water data set comprises energies and forces of 9,189 condensed-phase structures. The data was obtained in an iterative procedure described in detail in Ref. [4]. The final ANN potential was employed in Refs. [4,5].

Initial structures (iteration 0) were obtained from classical and path integral AIMD simulations of bulk liquid water in a cubic box containing 64 water molecules at 300 K as reported in Ref. [6]. Distorted configurations with higher forces were added by randomly displacing the Cartesian coordinates. Iteration 1 contains 500 configurations from MD simulations with the fully flexible SPC/E flex water model [7] employing a 25% increased water density (80 water molecules) and elevated temperatures (T= 500 K) to sample highly repulsive configurations. Structures in iteration 2 were obtained by classical MD simulations with preliminary ANN potentials at T= 300, 325, 350, and 370 K using cubic 64-molecule boxes and experimental densities. Iteration 3 data contains structures from preliminary ANN simulations with classical and quantum nuclei at a wide range of temperatures (T= 258, 268, 280, 290, 300, 310, 320, 330, 340, 350, 360, and 370 K) using experimental densities.

Energies and atomic forces were calculated with CP2K [8,9] using the revPBE exchange-correlation functional [10,11] with D3 dispersion correction [12] following Ref. [4]. Atomic cores were represented using the dual-space Goedecker-Teter-Hutter pseudopotentials [13], Kohn-Sham orbitals were expanded in the TZV2P basis set within the GPW method [14], and the density was represented by an auxiliary plane-wave basis with a cutoff of 400 Ry.

Amorphous LiSi: The amorphous LiSi data set comprises ~45,000 atomic bulk, surface, and cluster structures of elemental Li and Si, and Li(x)Si alloys of various compositions (0.0 ≤ x ≤ 4.75) as well as the corresponding energies and interatomic forces, which were generated using an iterative approach. An initial data set was based on the crystal structures of Li, Si, and the LiSi alloys with distorted lattice parameters and perturbed atomic positions. Additional partially delithiated alloy structures were generated using an evolutionary algorithm and further refined by including structures from ANN-potential MD simulations. Further details are given in references [15, 16].

The energies and forces of the LiSi structures were obtained from DFT calculations using the PBE [10] exchange-correlation functional and PAW pseudopotentials [17], as implemented in VASP [18,19]. We employed a plane-wave basis set with a cutoff of 520 eV and a uniform gamma-centered k-point grid with a mesh density corresponding to a number of k points of at least 1000 divided by the number of atoms. Most calculations in this data set were obtained from single-point DFT calculations. For ground state crystal structures, the atomic positions and lattice parameters were optimized until residual forces were below 20 meV/Å.

[1] A. Kokalj, J. Mol. Graphics Modell. 17, 176-179 (1999).
[2] N. Artrith, A. Urban, Comput. Mater. Sci. 114, 135-150 (2016).
[3] N. Artrith, A. Urban, G. Ceder, Phys. Rev. B 96, 014112 (2017).
[4] T. Morawietz, O. Marsalek, S. R. Pattenaude, L. M. Streacker, D. Ben-Amotz, and T. E. Markland, J. Phys. Chem. Lett. 9, 851 (2018).
[5] T. Morawietz, A. S. Urbina, P. K. Wise, X. Wu, W. Lu, D. Ben-Amotz, and T. E. Markland, J. Phys. Chem. Lett. 10, 6067 (2019).
[6] O. Marsalek and T. E. Markland, J. Phys. Chem. Lett. 8, 1545 (2017).
[7] X. B. Zhang, Q. L. Liu, and A. M. Zhu, Fluid Ph. Equilibria 262, 210 (2007).
[8] J. VandeVondele, M. Krack, F. Mohamed, M. Parrinello, T. Chassaing, and J. Hutter, Comput. Phys. Commun. 167, 103 (2005).
[9] J. Hutter, M. Iannuzzi, F. Schiffmann, and J. VandeVondele, WIRES Comput. Mol. Sci. 4, 15 (2014).
[10] J. P. Perdew, K. Burke, and M. Ernzerhof, Phys. Rev. Lett. 77, 3865 (1996).
[11] Y. Zhang and W. Yang, Phys. Rev. Lett. 80, 890 (1998).
[12] S. Grimme, J. Antony, S. Ehrlich, and H. Krieg, J. Chem. Phys. 132, 154104 (2010).
[13] S. Goedecker, M. Teter, and J. Hutter, Phys. Rev. B 54, 1703 (1996).
[14] B. G. Lippert, J. Hutter, and M. Parrinello, Mol. Phys. 92, 477 (1997).
[15] N. Artrith, A. Urban, G. Ceder, J. Chem. Phys. 148, 241711 (2018).
[16]  N. Artrith, A. Urban, Y. Wang, G. Ceder, https://arxiv.org/abs/1901.09272 (2019).
[17] P. E. Blöchl, Phys. Rev. B 50, 17953–17979 (1994).
[18] G. Kresse, J. Furthmüller, Phys. Rev. B 54, 11169-11186 (1996).
[19] G. Kresse, J. Furthmüller, Comput. Mater. Sci. 6, 15-50 (1996).

en: AENET-LAMMPS and AENET-TINKER: interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

aenet

display_name: aenet

amorphous-lisi

display_name: amorphous-lisi

liquid-water

display_name: liquid-water

path-integral-aimd

display_name: path-integral-aimd

vasp

display_name: vasp