cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:hf-qx
title: Improved wetting model for the prediction of topography and dimensionality of superomniphobic surfaces

description: This code calculates the contact angle formed between a sessile drop of an arbitrarily defined liquid and a rough surface based on our improved Cassie-Baxter wetting model (https://doi.org/10.1088/2051-672X/ab9419). The topography of the surface needs to be predefined into the input file and may be any of the types: a) 2D pillars, b) fibers, c) sinusoids, d) 3D pillars. Although, theoretically, our model can be applied to topographies with arbitrarily large multiplicity, here the code was devised such that it considers up to three-level topographies hierarchically placed on top of one another. In the "Input" directory, three input files are given for single, two-level and three-level topographies, respectively. In multilevel topographies, the above-mentioned topography types may be combined at will. So, for example, we may have a three-level topography with sinusoidal pulses as the coarser level, fibers as the middle-level and 2D pillars as the finest level. Similarly, two-level and single-level topographies are also possible. The definitions of the multiplicity level and topography types proceed in the input file.
DOI: 10.24435/materialscloud:96-09
title: Finding new crystalline compounds using chemical similarity

description: We proposed an efficient high-throughput scheme for the discovery of new stable crystalline phases. Our approach was based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data mining experimental databases. In this way we build more than 250k possible crystal phases, with almost 20k that are on the convex hull of stability. This dataset contains the optimized structure and the energy of these 250k materials calculated with the PBE approximation, in a format that is convenient for data-mining or for machine-learning applications.
DOI: 10.24435/materialscloud:ff-09
title: Finding new crystalline compounds using chemical similarity

description: We proposed an efficient high-throughput scheme for the discovery of new stable crystalline phases. Our approach was based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data mining experimental databases. In this way we build more than 250k possible crystal phases, with almost 20k that are on the convex hull of stability. This dataset contains the optimized structure and the energy of these 250k materials calculated with the PBE approximation, in a format that is convenient for data-mining or for machine-learning applications.
DOI: 10.24435/materialscloud:z9-zr
title: Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CH₃SO₃H and H₂O₂ in phenol

description: Set of inputs to perform the calculations reported in the paper.
The i-pi input enables to perform molecular dynamics / metadynamics / REMD / PIMD simulations, with adequate thermostats.
The DFTB and LAMMPS input respectively enable to calculate force and energies within the DFTB and Neural Network Forcefield frameworks.
The CP2K input files enable to calculate force and energies at PBE and PBE0 level. The latter is used as the reference to train the neural network correction on top of DFTB.

Brief description of the work: We present a generally-applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in explicit solvent using H₂O₂ and CH₃SO₃H in phenol as an example. In order to address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density-functional tight-binding (DFTB) level. Energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant non-covalent interactions in the mixture, also considering nuclear quantum effects.
DOI: 10.24435/materialscloud:hb-v3
title: Vertex function compliant with the Ward identity for quasiparticle self-consistent calculations beyond GW

description: We extend the quasiparticle self-consistent approach beyond the GW approximation by using a range-separated vertex function. The developed approach yields band gaps, dielectric constants, and band positions with an accuracy similar to highest-level electronic-structure calculations without exceeding the cost of regular quasiparticle self-consistent GW. We introduce an exchange-correlation kernel that accounts for the vertex over the full spatial range. In the long range it complies with the Ward identity, while it is approximated through the adiabatic local density functional in the short range. In this approach, the renormalization factor is balanced and the higher-order diagrams are effectively taken into account.
DOI: 10.24435/materialscloud:am-kf
title: Vertex function compliant with the Ward identity for quasiparticle self-consistent calculations beyond GW

description: We extend the quasiparticle self-consistent approach beyond the GW approximation by using a range-separated vertex function. The developed approach yields band gaps, dielectric constants, and band positions with an accuracy similar to highest-level electronic-structure calculations without exceeding the cost of regular quasiparticle self-consistent GW. We introduce an exchange-correlation kernel that accounts for the vertex over the full spatial range. In the long range it complies with the Ward identity, while it is approximated through the adiabatic local density functional in the short range. In this approach, the renormalization factor is balanced and the higher-order diagrams are effectively taken into account.
DOI: 10.24435/materialscloud:6p-ga
title: Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CH₃SO₃H and H₂O₂ in phenol

description: Set of inputs to perform the calculations reported in the paper.
The i-pi input enables to perform molecular dynamics / metadynamics / REMD / PIMD simulations, with adequate thermostats.
The DFTB and LAMMPS input respectively enable to calculate force and energies within the DFTB and Neural Network Forcefield frameworks.
The CP2K input files enable to calculate force and energies at PBE and PBE0 level. The latter is used as the reference to train the neural network correction on top of DFTB.

Brief description of the work: We present a generally-applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in explicit solvent using H₂O₂ and CH₃SO₃H in phenol as an example. In order to address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density-functional tight-binding (DFTB) level. Energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant non-covalent interactions in the mixture, also considering nuclear quantum effects.
DOI: 10.24435/materialscloud:2x-7x
title: Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CH₃SO₃H and H₂O₂ in phenol

description: Set of inputs to perform the calculations reported in the paper.
The i-pi input enables to perform molecular dynamics / metadynamics / REMD / PIMD simulations, with adequate thermostats.
The DFTB and LAMMPS input respectively enable to calculate force and energies within the DFTB and Neural Network Forcefield frameworks.
The CP2K input files enable to calculate force and energies at PBE and PBE0 level. The latter is used as the reference to train the neural network correction on top of DFTB.

Brief description of the work: We present a generally-applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in explicit solvent using H₂O₂ and CH₃SO₃H in phenol as an example. In order to address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density-functional tight-binding (DFTB) level. Energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant non-covalent interactions in the mixture, also considering nuclear quantum effects.
DOI: 10.24435/materialscloud:xx-et
title: Magnetic and electronic properties at the γ-Al2O3/SrTiO3 interface

description: The magnetic and electronic nature of the γ-Al2O3/SrTiO3 spinel/perovskite interface is explored by means of x-ray absorption spectroscopy. Polarized x-ray techniques combined with atomic multiplet calculations reveal localized magnetic moments assigned to Ti3+ at the interface with equivalent size for in- and out-of-plane magnetic field directions. Although magnetic fingerprints are revealed, the Ti3+ magnetism can be explained by a paramagnetic response at low temperature under applied magnetic fields. Modeling the x-ray linear dichroism results in a Delta0 ∼ 1.9 eV splitting between the t2g and eg states for the Ti4+ 3d0 orbitals. In addition these results indicate that the lowest energy states have the out-of-plane dxz/dyz symmetry. The isotropic magnetic moment behavior and the lowest energy dxz/dyz states are in contrast to the observations for the two-dimensional electron gas at the perovskite/perovskite interface of LaAlO3/SrTiO3 that exhibits an anisotropic magnetic dxy ground state.
DOI: 10.24435/materialscloud:hn-cr
title: Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CH₃SO₃H and H₂O₂ in phenol

description: Set of inputs to perform the calculations reported in the paper.
The i-pi input enables to perform molecular dynamics / metadynamics / REMD / PIMD simulations, with adequate thermostats.
The DFTB and LAMMPS input respectively enable to calculate force and energies within the DFTB and Neural Network Forcefield frameworks.
The CP2K input files enable to calculate force and energies at PBE and PBE0 level. The latter is used as the reference to train the neural network correction on top of DFTB.

Brief description of the work: We present a generally-applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in explicit solvent using H₂O₂ and CH₃SO₃H in phenol as an example. In order to address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density-functional tight-binding (DFTB) level. Energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant non-covalent interactions in the mixture, also considering nuclear quantum effects.
DOI: 10.24435/materialscloud:vd-yv
title: Magnetic and electronic properties at the γ-Al2O3/SrTiO3 interface

description: The magnetic and electronic nature of the γ-Al2O3/SrTiO3 spinel/perovskite interface is explored by means of x-ray absorption spectroscopy. Polarized x-ray techniques combined with atomic multiplet calculations reveal localized magnetic moments assigned to Ti3+ at the interface with equivalent size for in- and out-of-plane magnetic field directions. Although magnetic fingerprints are revealed, the Ti3+ magnetism can be explained by a paramagnetic response at low temperature under applied magnetic fields. Modeling the x-ray linear dichroism results in a Delta0 ∼ 1.9 eV splitting between the t2g and eg states for the Ti4+ 3d0 orbitals. In addition these results indicate that the lowest energy states have the out-of-plane dxz/dyz symmetry. The isotropic magnetic moment behavior and the lowest energy dxz/dyz states are in contrast to the observations for the two-dimensional electron gas at the perovskite/perovskite interface of LaAlO3/SrTiO3 that exhibits an anisotropic magnetic dxy ground state.
DOI: 10.24435/materialscloud:v7-qg
title: Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CH₃SO₃H and H₂O₂ in phenol

description: Set of inputs to perform the calculations reported in the paper.
The i-pi input enables to perform molecular dynamics / metadynamics / REMD / PIMD simulations, with adequate thermostats.
The DFTB and LAMMPS input respectively enable to calculate force and energies within the DFTB and Neural Network Forcefield frameworks.
The CP2K input files enable to calculate force and energies at PBE and PBE0 level. The latter is used as the reference to train the neural network correction on top of DFTB.

Brief description of the work: We present a generally-applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in explicit solvent using H₂O₂ and CH₃SO₃H in phenol as an example. In order to address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density-functional tight-binding (DFTB) level. Energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant non-covalent interactions in the mixture, also considering nuclear quantum effects.
DOI: 10.24435/materialscloud:7e-pc
title: Computational design of moiré assemblies aided by artificial intelligence

description: Two-dimensional (2D) layered materials offer a materials platform with potential applications from energy to information processing devices. Although some single- and few-layer forms of materials such as graphene and transition metal dichalcogenides have been realized and thoroughly studied, the space of arbitrarily layered assemblies is still mostly unexplored. The main goal of this work is to demonstrate precise control of layered materials' electronic properties through careful choice of the constituent layers, their stacking, and relative orientation. Physics-based and AI-driven approaches for the automated planning, execution, and analysis of electronic structure calculations are applied to layered assemblies based on prototype one-dimensional (1D) materials and realistic 2D materials. We find it is possible to routinely generate moiré band structures in 1D with desired electronic characteristics such as a band gap of any value within a large range, even with few layers and materials (here, four and six, respectively). We argue that this tunability extends to 2D materials by showing the essential physical ingredients are already evident in calculations of two-layer MoS2 and multi-layer graphene moiré assemblies.
DOI: 10.24435/materialscloud:pm-8p
title: Asymmetric elimination reaction on chiral metal surfaces

description: The production of enantiopure materials and molecules is of uttermost relevance in research and industry in numerous contexts, ranging from non-linear optics to asymmetric synthesis. In the context of the latter, we have investigated dehalogenation, which is an essential reaction step for a broad class of chemical reactions. Specifically, dehalogenation of prochiral 5-bromo-7-methylbenz(a)anthracene (BMA) on prototypical, chiral, intermetallic PdGa{111} surfaces under ultrahigh vacuum conditions. Enantioselective halogen elimination is demonstrated by combining temperature-programmed x-ray photoelectron spectroscopy, scanning probe microscopy, and density functional theory. On the PdGa{111} surfaces, the difference in debromination temperatures for the two BMA surface enantiomers amounts up to unprecedented 46 K. The significant dependence of the dehalogenation temperature of the BMA surface enantiomers on the atomic termination of the PdGa{111} surfaces, implies that the ensemble effect is pronounced in this reaction step. These findings evidence enantiospecific control and hence promote intrinsically chiral crystals for asymmetric on-surface synthesis. 
The record contains input files to reproduce the calculations discussed in the manuscript and the raw data of the experimental images discussed.
DOI: 10.24435/materialscloud:t3-xv
title: Computational design of moiré assemblies aided by artificial intelligence

description: Two-dimensional (2D) layered materials offer a materials platform with potential applications from energy to information processing devices. Although some single- and few-layer forms of materials such as graphene and transition metal dichalcogenides have been realized and thoroughly studied, the space of arbitrarily layered assemblies is still mostly unexplored. The main goal of this work is to demonstrate precise control of layered materials' electronic properties through careful choice of the constituent layers, their stacking, and relative orientation. Physics-based and AI-driven approaches for the automated planning, execution, and analysis of electronic structure calculations are applied to layered assemblies based on prototype one-dimensional (1D) materials and realistic 2D materials. We find it is possible to routinely generate moiré band structures in 1D with desired electronic characteristics such as a band gap of any value within a large range, even with few layers and materials (here, four and six, respectively). We argue that this tunability extends to 2D materials by showing the essential physical ingredients are already evident in calculations of two-layer MoS2 and multi-layer graphene moiré assemblies.
DOI: 10.24435/materialscloud:fm-w8
title: Asymmetric elimination reaction on chiral metal surfaces

description: The production of enantiopure materials and molecules is of uttermost relevance in research and industry in numerous contexts, ranging from non-linear optics to asymmetric synthesis. In the context of the latter, we have investigated dehalogenation, which is an essential reaction step for a broad class of chemical reactions. Specifically, dehalogenation of prochiral 5-bromo-7-methylbenz(a)anthracene (BMA) on prototypical, chiral, intermetallic PdGa{111} surfaces under ultrahigh vacuum conditions. Enantioselective halogen elimination is demonstrated by combining temperature-programmed x-ray photoelectron spectroscopy, scanning probe microscopy, and density functional theory. On the PdGa{111} surfaces, the difference in debromination temperatures for the two BMA surface enantiomers amounts up to unprecedented 46 K. The significant dependence of the dehalogenation temperature of the BMA surface enantiomers on the atomic termination of the PdGa{111} surfaces, implies that the ensemble effect is pronounced in this reaction step. These findings evidence enantiospecific control and hence promote intrinsically chiral crystals for asymmetric on-surface synthesis. 
The record contains input files to reproduce the calculations discussed in the manuscript and the raw data of the experimental images discussed.
DOI: 10.24435/materialscloud:3s-7w
title: Atomic structures of 100nm x 100nm large oxide-, nitride-, sulfide-, and phosphide-derived copper surfaces

description: Cif files of relaxed "derived" copper surfaces from Cu2O, Cu2S, Cu3N, and Cu3P using EMT and published in J Gauthier, JH Stenlid, F Abild-Pedersen, M Head-Gordon, AT Bell, ACS Energy Letters, "The role of roughening to enhance selectivity to C2+ products during CO2 electroreduction on copper" (2021). These structures were used to evaluate the effect of roughening on catalytic selectivity of copper in electrochemcial CO2 reduction into valuable products such as fuels and commodity chemicals.