cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:5t-qn
title: Optical imaging and spectroscopy of atomically precise armchair graphene nanoribbons

description: 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.
DOI: 10.24435/materialscloud:p9-eq
title: Optical imaging and spectroscopy of atomically precise armchair graphene nanoribbons

description: 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.
DOI: 10.15468/dl.k47bqf
title: Occurrence Download

description: A dataset containing 3737 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Houttuynia cordata Thunb."
 ]
}

 The dataset includes 3737 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067080-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.24435/materialscloud:f4-wv
title: Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen

description: 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.
DOI: 10.24435/materialscloud:2019.0070/v1
title: Comparison of computational methods for the electrochemical stability window of solid-state electrolyte materials

description: Superior stability and safety are key promises attributed to all-solid-state batteries (ASSBs) containing solid-state electrolyte (SSE) in comparison to their conventional counterparts utilizing liquid electrolyte. To unfold the full potential of ASSBs, SSE materials are desirable that are stable in contact with both the low and the high potential electrode. The electrochemical stability window is conveniently used to assess the SSE--electrode interface stability. In the present work, we review the most important methods to compute the SSE stability window. We find that the stoichiometry stability method represents a bridge between HOMO--LUMO method and phase stability method (grand canonical phase diagram). We further provide implementations of these methods for SSE material screening and we compare their results for the relevant Li- and Na-SSE materials LGPS, LIPON, LLZO, LLTO, LATP, LISICON, and NASICON.
DOI: 10.24435/materialscloud:kn-2v
title: Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen

description: 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.
DOI: 10.24435/materialscloud:rt-ek
title: Comparison of computational methods for the electrochemical stability window of solid-state electrolyte materials

description: Superior stability and safety are key promises attributed to all-solid-state batteries (ASSBs) containing solid-state electrolyte (SSE) in comparison to their conventional counterparts utilizing liquid electrolyte. To unfold the full potential of ASSBs, SSE materials are desirable that are stable in contact with both the low and the high potential electrode. The electrochemical stability window is conveniently used to assess the SSE--electrode interface stability. In the present work, we review the most important methods to compute the SSE stability window. We find that the stoichiometry stability method represents a bridge between HOMO--LUMO method and phase stability method (grand canonical phase diagram). We further provide implementations of these methods for SSE material screening and we compare their results for the relevant Li- and Na-SSE materials LGPS, LIPON, LLZO, LLTO, LATP, LISICON, and NASICON.
DOI: 10.24435/materialscloud:sh-79
title: Ab initio real-time quantum dynamics of charge carriers in momentum space

description: Application of the nonadiabatic molecular dynamics (NAMD) approach is severely limited to studying carrier dynamics in the momentum space, since a supercell is required to sample the phonon excitation and electron-phonon (e-ph) interaction at different momenta in a molecular dynamics simulation. Here, we develop an ab initio approach for the real-time quantum dynamics for charge carriers in the momentum space (NAMD_k) by directly introducing the e-ph coupling into the Hamiltonian based on the harmonic approximation. The NAMD_k approach maintains the quantum zero-point energy and proper phonon dispersion, and includes memory effects of phonon excitation. The application of NAMD_k to the hot carrier dynamics in graphene reveals the phonon-specific relaxation mechanism. An energy threshold of 0.2eV, defined by two optical phonon modes strongly coupled to the electrons, separates the hot electron relaxation into fast and slow regions with the lifetimes of pico- and nano-seconds, respectively. The NAMD_k approach provides a powerful tool to understand real-time carrier dynamics in the momentum space for different materials.
DOI: 10.24435/materialscloud:2020.0002/v2
title: Regioselective 3-O-Substitution of Unprotected Thiodigalactosides: Direct Route to Galectin Inhibitors

description: Regioselective derivatization of oligosaccharides is a challenging issue in carbohydrate chemistry. A commonly required series of (de)protection steps substantially lowers synthetic yields and increases time demands. We present here a regioselective one-step introduction of benzylic substituents at 3-hydroxy moieties of beta-D-galactopyranosyl-(1<->1)-thio-beta-D-galactopyranoside (TDG) employing tin butyl oxide in fair isolated yields. These glycomimetics act as inhibitors of galectins - human lectins, which are biomedically attractive targets for therapeutic inhibition. The affinity of prepared glycomimetics to recombinant galectin-1 and galectin-3 was studied in ELISA-type assay and their inhibitory potential was also demonstrated on the surface of a model HEK293 cell line. The results of biological experiments were correlated with data from molecular modelling with both galectins. The present work reveals a facile and elegant synthetic route for the preparation of glycomimetics that exhibit differing selectivity and affinity to various galectins depending on the choice of 3-O-substitution.
In order to rationalize structure-activity relationships of tested ligands we carried out a series of molecular dynamics simulations and quantum chemical calculations. We present input and output files for quantum chemical geometry optimizations and calculations of interactions energies. These calculations were carried out in Gaussian 09 package. These results can be visualized using Molden, Molekel or other programs.
Molecular dynamics simulations were carried out using Gromacs 5.1.3 package. Initial structures of production simulations and trajectories (without water) are presented. The results can be visualized by VMD or other structure visualization packages with a support for trajectory input.
DOI: 10.24435/materialscloud:w5-d6
title: Second-harmonic generation tensors from high-throughput density-functional perturbation theory

description: 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.
DOI: 10.24435/materialscloud:yh-2d
title: Second-harmonic generation tensors from high-throughput density-functional perturbation theory

description: 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.
DOI: 10.24435/materialscloud:vg-ya
title: High-throughput computational screening for solid-state Li-ion conductors

description: 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.
DOI: 10.24435/materialscloud:jk-9v
title: High-throughput computational screening for solid-state Li-ion conductors

description: 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.
DOI: 10.24435/materialscloud:td-hf
title: Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

description: Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts' local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of surface atomic configurations. To address this challenge, we present the Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that can account for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt3Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combine with the low symmetry of the alloy substrate to produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, wherein the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions result in the configurational complexity. In both cases, the ACE-GCN model, having trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully ranks the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions.
DOI: 10.24435/materialscloud:fv-f5
title: Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

description: Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts' local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of surface atomic configurations. To address this challenge, we present the Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that can account for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt3Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combine with the low symmetry of the alloy substrate to produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, wherein the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions result in the configurational complexity. In both cases, the ACE-GCN model, having trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully ranks the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions.
DOI: 10.24435/materialscloud:2019.0068/v1
title: The solid-state Li-ion conductor Li7TaO6: A combined computational and experimental study

description: 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.
DOI: 10.24435/materialscloud:2020.0046/v1
title: Pure Magnesium DFT calculations for interatomic potential fitting

description: 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.
DOI: 10.24435/materialscloud:s8-e7
title: The solid-state Li-ion conductor Li7TaO6: A combined computational and experimental study

description: 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.
DOI: 10.24435/materialscloud:2019.0003/v1
title: Emergence of hidden phases of methylammonium lead-iodide (CH3NH3PbI) upon compression

description: 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.
DOI: 10.24435/materialscloud:8f-1s
title: Pure Magnesium DFT calculations for interatomic potential fitting

description: 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
DOI: 10.24435/materialscloud:xd-vh
title: Emergence of hidden phases of methylammonium lead-iodide (CH3NH3PbI) upon compression

description: 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.