cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:2019.0004/v4
title: Making the best of a bad situation: a multiscale approach to free energy calculation

description: Many enhanced sampling techniques rely on the identification of a number of collective variables that describe all the slow modes of the system. By constructing a bias potential in this reduced space one is then able to sample efficiently and reconstruct the free energy landscape. In methods like metadynamics, the quality of these collective variables plays a key role in convergence efficiency. Unfortunately in many systems of interest it is not possible to identify an optimal collective variable, and one must deal with the non-ideal situation of a system in which some slow modes are not accelerated. 
We propose a two-step approach in which, by taking into account the residual multiscale nature of the problem, one is able to significantly speed up convergence. To do so, we combine an exploratory metadynamics run with an optimization of the free energy difference between metastable states, based on the recently proposed variationally enhanced sampling method. This new method is well parallelizable and is especially suited for complex systems, because of its simplicity and clear underlying physical picture.
DOI: 10.24435/materialscloud:z9-89
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:2019.0089/v1
title: Stress-dependence of generalized stacking fault energies: a DFT study

description: 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.
DOI: 10.24435/materialscloud:2019.0012/v1
title: Tailoring Bond Topologies in Open-Shell Graphene Nanostructures

description: 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
DOI: 10.24435/materialscloud:gq-t9
title: Stress-dependence of generalized stacking fault energies: a DFT study

description: 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.
DOI: 10.24435/materialscloud:9c-8k
title: Tailoring Bond Topologies in Open-Shell Graphene Nanostructures

description: 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
DOI: 10.24435/materialscloud:aw-d3
title: High-throughput screening of 2D materials identifies p-type monolayer WS2 as potential ultra-high mobility semiconductor

description: 2D semiconductors are considered as a promising alternative to silicon for future electronics. This class of materials possesses different advantages including atomically sharp surfaces and the ability to scale channel thickness down to a single layer. However, they typically exhibit lower charge carrier mobility as well as higher contact resistance compared to 3D semiconductors, which deters the development of high-performance devices at scale. In this work, we searched for high-mobility 2D materials by combining high-throughput screening approach and advanced transport calculations based on the ab initio Boltzmann transport equation. Based on our calculations, we identified several promising candidates channel materials, and in particular monolayer WS₂ which exhibits a phonon-limited hole mobility in excess of 1300 cm²/Vs. Our work suggests that WS₂ can be ideal for channel of high-performance 2D transistors with Ohmic contacts and low defect density. This work has been published in [npj Comput. Mater. 10, 229 (2024)].
DOI: 10.24435/materialscloud:m2-vc
title: High-throughput screening of 2D materials identifies p-type monolayer WS2 as potential ultra-high mobility semiconductor

description: 2D semiconductors are considered as a promising alternative to silicon for future electronics. This class of materials possesses different advantages including atomically sharp surfaces and the ability to scale channel thickness down to a single layer. However, they typically exhibit lower charge carrier mobility as well as higher contact resistance compared to 3D semiconductors, which deters the development of high-performance devices at scale. In this work, we searched for high-mobility 2D materials by combining high-throughput screening approach and advanced transport calculations based on the ab initio Boltzmann transport equation. Based on our calculations, we identified several promising candidates channel materials, and in particular monolayer WS₂ which exhibits a phonon-limited hole mobility in excess of 1300 cm²/Vs. Our work suggests that WS₂ can be ideal for channel of high-performance 2D transistors with Ohmic contacts and low defect density. This work has been published in [npj Comput. Mater. 10, 229 (2024)].
DOI: 10.24435/materialscloud:2020.0003/v1
title: Nonempirical hybrid functionals for band gaps of inorganic metal-halide perovskites

description: Nonempirical hybrid functionals are investigated for band-gap predictions of inorganic metal-halide perovskites belonging to the class CsBX3 , with B = Ge, Sn, Pb and X = Cl, Br, I. We consider both global and range-separated hybrid functionals and determine the parameters through two different schemes. The first scheme is based on the static screening response of the material and thus yields dielectric-dependent hybrid functionals. The second scheme defines the hybrid functionals through the enforcement of Koopmans' condition for localized defect states. We also carry out quasiparticle self-consistent GW calculations with vertex corrections to establish state-of-the-art references. For the investigated class of materials, dielectric-dependent functionals and those fulfilling Koopmans' condition yield band gaps of comparable accuracy (∼0.2 eV), but the former only require calculations for the primitive unit cell and are less subject to the specifics of the material.
DOI: 10.24435/materialscloud:dg-ca
title: Nonempirical hybrid functionals for band gaps of inorganic metal-halide perovskites

description: Nonempirical hybrid functionals are investigated for band-gap predictions of inorganic metal-halide perovskites belonging to the class CsBX3 , with B = Ge, Sn, Pb and X = Cl, Br, I. We consider both global and range-separated hybrid functionals and determine the parameters through two different schemes. The first scheme is based on the static screening response of the material and thus yields dielectric-dependent hybrid functionals. The second scheme defines the hybrid functionals through the enforcement of Koopmans' condition for localized defect states. We also carry out quasiparticle self-consistent GW calculations with vertex corrections to establish state-of-the-art references. For the investigated class of materials, dielectric-dependent functionals and those fulfilling Koopmans' condition yield band gaps of comparable accuracy (∼0.2 eV), but the former only require calculations for the primitive unit cell and are less subject to the specifics of the material.
DOI: 10.24435/materialscloud:zb-ed
title: Design rules for interconnects based on graphene nanoribbon junctions

description: Graphene nanoribbons (GNRs) produced by means of bottom-up chemical self-assembly are considered promising candidates for the next-generation nanoelectronic devices. We address the electronic transport properties of angled two-terminal GNR junctions, which are inevitable in the interconnects in graphene-based integrated circuits. We construct a library of over 400000 distinct configurations of 60° and 120° junctions connecting armchair GNRs of different widths. Numerical calculations combining the tight-binding approximation and the Green's function formalism allow identifying numerous junctions with conductance close to the limit defined by the GNR leads. Further analysis reveals underlying structure-property relationships with crucial roles played by the bipartite symmetry of graphene lattice and the presence of resonant states localized at the junction. In particular, we discover and explain the phenomenon of binary conductance in 120° junctions connecting metallic GNR leads that guarantee maximum possible conductance. Overall, our study defines the guidelines for engineering GNR junctions with desired electrical properties.
DOI: 10.24435/materialscloud:2019.0004/v1
title: Making the best of a bad situation: a multiscale approach to free energy calculation

description: Many enhanced sampling techniques rely on the identification of a number of collective variables that describe all the slow modes of the system. By constructing a bias potential in this reduced space one is then able to sample efficiently and reconstruct the free energy landscape. In methods like metadynamics, the quality of these collective variables plays a key role in convergence efficiency. Unfortunately in many systems of interest it is not possible to identify an optimal collective variable, and one must deal with the non-ideal situation of a system in which some slow modes are not accelerated. 
We propose a two-step approach in which, by taking into account the residual multiscale nature of the problem, one is able to significantly speed up convergence. To do so, we combine an exploratory metadynamics run with an optimization of the free energy difference between metastable states, based on the recently proposed variationally enhanced sampling method. This new method is well parallelizable and is especially suited for complex systems, because of its simplicity and clear underlying physical picture.
DOI: 10.24435/materialscloud:21-rc
title: Design rules for interconnects based on graphene nanoribbon junctions

description: Graphene nanoribbons (GNRs) produced by means of bottom-up chemical self-assembly are considered promising candidates for the next-generation nanoelectronic devices. We address the electronic transport properties of angled two-terminal GNR junctions, which are inevitable in the interconnects in graphene-based integrated circuits. We construct a library of over 400000 distinct configurations of 60° and 120° junctions connecting armchair GNRs of different widths. Numerical calculations combining the tight-binding approximation and the Green's function formalism allow identifying numerous junctions with conductance close to the limit defined by the GNR leads. Further analysis reveals underlying structure-property relationships with crucial roles played by the bipartite symmetry of graphene lattice and the presence of resonant states localized at the junction. In particular, we discover and explain the phenomenon of binary conductance in 120° junctions connecting metallic GNR leads that guarantee maximum possible conductance. Overall, our study defines the guidelines for engineering GNR junctions with desired electrical properties.
DOI: 10.24435/materialscloud:2019.0081/v1
title: Relative Abundance of Z2 Topological Order in Exfoliable Two-Dimensional Insulators

description: Quantum spin Hall insulators (QSHIs) make up a class of two-dimensional materials with a finite electronic band gap in the bulk and gapless helical edge states.  Some of the phenomena that can be hosted in these materials, from one-dimensional low-dissipation electronic transport to spin filtering, could be promising for many technological applications in the fields of electronics, spintronics, and topological quantum computing. Nevertheless, the rarity of two-dimensional materials that can exhibit nontrivial topological order at room temperature hinders development. In the publication, we report on our screening of a comprehensive database we recently identified of 1825 monolayers that can be exfoliated from experimentally known compounds to search for novel quantum spin Hall insulators.  In this entry we provide the AiiDA database with the calculations of the DFT band structures (both with and without spin-orbit coupling) and the DFPT phonon dispersions for the QSHI candidates that we identify. All the data are also displayed in the dedicated DISCOVER section "2D topological insulators" https://www.materialscloud.org/discover/2dtopo/dashboard/ptable .
DOI: 10.24435/materialscloud:2019.0082/v2
title: Relative Stability of Near-Surface Oxygen Vacancies at the CeO2 (111) Surface upon Zirconium Doping

description: The effects of Zr doping on the stability of the CeO2(111) surface as a function of the dopant concentration and distribution, as well as on the relative stability of surface and subsurface oxygen vacancies, were studied by means of density functional theory (DFT+U) calculations. For a given Zr content, the more stable structures do not correspond to those configurations with Zr located in the topmost O-Ce-O trilayer (TL1), but in inner layers, and the stability decreases with increasing Zr concentration. For the undoped CeO2(111) surface, the preference of subsurface vacancies with next-nearest neighbor (NNN) Ce3+ configuration has earlier been predicted. For the Zr-doped surface, the formation of vacancies was studied using a surface unit cell with 2x2 periodicity, and it was found that the most stable configuration corresponds to the  Zr atom located in the surface layer (TL1) neighboring a subsurface oxygen vacancy with NNN Ce3+, being the  formation energy equal to 1.16 eV. The corresponding surface oxygen vacancy is 0.16 eV less stable. These values are by 0.73 and 0.92 eV, respectively, smaller than the corresponding ones for the pure CeO2(111) surface. Moreover, when Zr is located in TL2 the subsurface vacancy becomes by 0.10 eV less stable, compared to Zr in the TL1. The Ce3+ preference for the next-nearest neighbor cationic sites to both surface and subsurface vacancies at CeO2 (111), becomes more pronounced upon Zr doping. The results are explained in terms of Zr- and vacancy-induced lattice relaxation effects.
DOI: 10.24435/materialscloud:2020.0004/v1
title: Koopmans-Compliant Functionals and Potentials and Their Application to the GW100 Test Set

description: Koopmans-compliant (KC) functionals have been shown to provide accurate spectral properties through a generalized condition of piecewise linearity of the total energy as a function of the fractional addition/removal of an electron to/from any orbital. We analyze the performance of different KC functionals on a large and standardized set of 100 molecules, the GW100 test set, comparing vertical ionization potentials (taken as opposite of the orbital energies) to those obtained from accurate quantum chemistry methods, and to experimental results. We find excellent agreement, with a mean absolute error of 0.20 eV for the KIPZ functional on the first ionization potential, which is state-of-the-art for both density functional theory (DFT)-based calculations and many-body perturbation theory. We highlight similarities and differences between KC functionals and other electronic-structure approaches, such as dielectric-dependent hybrid functionals and Green's function methods, both from a theoretical and from a practical point of view, arguing that KC potentials can be considered as local and orbital-dependent approximations to the electronic self-energy, already including approximate vertex corrections.
DOI: 10.24435/materialscloud:bw-jn
title: Koopmans-Compliant Functionals and Potentials and Their Application to the GW100 Test Set

description: Koopmans-compliant (KC) functionals have been shown to provide accurate spectral properties through a generalized condition of piecewise linearity of the total energy as a function of the fractional addition/removal of an electron to/from any orbital. We analyze the performance of different KC functionals on a large and standardized set of 100 molecules, the GW100 test set, comparing vertical ionization potentials (taken as opposite of the orbital energies) to those obtained from accurate quantum chemistry methods, and to experimental results. We find excellent agreement, with a mean absolute error of 0.20 eV for the KIPZ functional on the first ionization potential, which is state-of-the-art for both density functional theory (DFT)-based calculations and many-body perturbation theory. We highlight similarities and differences between KC functionals and other electronic-structure approaches, such as dielectric-dependent hybrid functionals and Green's function methods, both from a theoretical and from a practical point of view, arguing that KC potentials can be considered as local and orbital-dependent approximations to the electronic self-energy, already including approximate vertex corrections.
DOI: 10.24435/materialscloud:2019.0017/v1
title: Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials

description: Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. We develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information can be learned for various multi-component amorphous material systems, which is difficult to obtain otherwise. We develop a software package "gdynet" at https://github.com/txie-93/gdynet which implements the graph dynamical networks algorithm. This record contains the MD trajectories of a Li-S toy system, a Si-Au binary system, and a polymer battery electrolyte system in a format designed for the "gdynet" package.
DOI: 10.24435/materialscloud:2019.0016/v1
title: Multithermal-multibaric molecular simulations from a variational principle

description: We present a method for performing multithermal-multibaric molecular dynamics simulations that sample entire regions of the temperature-pressure (TP) phase diagram. The method uses a variational principle [Valsson and Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to construct a bias that leads to a uniform sampling in energy and volume. The intervals of temperature and pressure are taken as inputs and the relevant energy and volume regions are determined on the fly. In this way the method guarantees adequate statistics for the chosen TP region. We show that our multithermal-multibaric simulations can be used to calculate all static physical quantities for all temperatures and pressures in the targeted region of the TP plane. We illustrate our approach by studying the density anomaly of TIP4P/Ice water.

This record includes input and output files, and Jupyter Notebooks describing the analysis of the simulations and the creation of the figures for the paper.
DOI: 10.24435/materialscloud:3q-6b
title: Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials

description: Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. We develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information can be learned for various multi-component amorphous material systems, which is difficult to obtain otherwise. We develop a software package "gdynet" at https://github.com/txie-93/gdynet which implements the graph dynamical networks algorithm. This record contains the MD trajectories of a Li-S toy system, a Si-Au binary system, and a polymer battery electrolyte system in a format designed for the "gdynet" package.