cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:41-nt
title: A New Kind of Atlas of Zeolite Building Blocks

description: 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.
DOI: 10.24435/materialscloud:2019.0049/v1
title: Lattice thermal conductivity of MgSiO3 post-perovskite under the lowermost mantle conditions from ab initio anharmonic lattice dynamics

description: 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.
DOI: 10.24435/materialscloud:fj-tk
title: Spin fluctuation induced Weyl semimetal state in the paramagnetic phase of EuCd2As2

description: 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.
DOI: 10.24435/materialscloud:t7-hb
title: Lattice thermal conductivity of MgSiO3 post-perovskite under the lowermost mantle conditions from ab initio anharmonic lattice dynamics

description: 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.
DOI: 10.24435/materialscloud:9p-j0
title: Large-cavity coronoids with different inner and outer edge structures

description: 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.
DOI: 10.24435/materialscloud:2018.0023/v1
title: Ab initio electronic structure of liquid water: Molecular dynamics snapshots

description: 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.
DOI: 10.24435/materialscloud:5q-yy
title: Large-cavity coronoids with different inner and outer edge structures

description: 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.
DOI: 10.6084/m9.figshare.29390507
title: Peer-reviewed literature

description: This file contains the information of all the peer-reviewed literature selected for this MLR.
DOI: 10.6084/m9.figshare.29390507.v1
title: Peer-reviewed literature

description: This file contains the information of all the peer-reviewed literature selected for this MLR.
DOI: 10.24435/materialscloud:p2-c5
title: Ab initio electronic structure of liquid water: Molecular dynamics snapshots

description: 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.
DOI: 10.24435/materialscloud:2018.0005/v2
title: High-throughput computational screening of nanoporous adsorbents for CO 2 capture from natural gas

description: 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.
DOI: 10.24435/materialscloud:e4-h1
title: Polygonal tessellations as predictive models of molecular monolayers

description: 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.
DOI: 10.24435/materialscloud:2018.0001/v4
title: A Standard Solid State Pseudopotentials (SSSP) library optimized for precision and efficiency

description: 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.

*** UPDATE April 2020 *** 
The zipped tarball archives and AiiDA export files had inconsistent internal formats and naming conventions, which made it difficult to work with them programmatically. In this update, the files are standardized according to the conventions that are detailed in the README.md file. Note that the actual content of pseudo potential files and the JSON metadata files has **not** changed, with the exception that the keys "cutoff" and "dual" in the JSON files have been replaced with "cutoff_wfc" and "cutoff_rho". Here the value of "cutoff_wfc" is equal to the old "cutoff", and "cutoff_rho" is equal to the product of the old "cutoff" and "dual". Besides that, the update concerns merely the renaming of certain files and the restructuring of archive formats.
DOI: 10.24435/materialscloud:h0-xy
title: Polygonal tessellations as predictive models of molecular monolayers

description: 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.
DOI: 10.24435/materialscloud:2017.0006/v1
title: Gaussian Approximation Potentials for iron from extended first-principles database (Data Download)

description: 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.
DOI: 10.24435/materialscloud:2019.0088/v1
title: Improving collective variables: The case of crystallization

description: 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.
DOI: 10.24435/materialscloud:w7-ep
title: Improving collective variables: The case of crystallization

description: 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.
DOI: 10.24435/materialscloud:2019.0009/v1
title: Energetics of the coupled electronic–structural transition in the rare-earth nickelates

description: 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.
DOI: 10.24435/materialscloud:1y-de
title: Energetics of the coupled electronic–structural transition in the rare-earth nickelates

description: 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.
DOI: 10.24435/materialscloud:1s-51
title: Dictionary of 140k GDB and ZINC derived AMONs

description: 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.
DOI: 10.24435/materialscloud:2020.0008/v1
title: Rare-earth magnetic nitride perovskites

description: We propose perovskite nitrides with magnetic rare-earth metals as novel materials with a range of technological applications. These materials appear to be thermodynamically stable and, in spite of possessing different crystal structures and different atomic environments, they retain the magnetic moment of the corresponding elemental rare-earth metal. We find both magnetic metals and semiconductors, with a wide range of magnetic moments and some systems posses record-high magnetic anisotropy energies. Further tuning of the electronic and magnetic properties can also be expected by doping with other rare-earths or by creating solid solutions. The synthesis of these exotic materials with unusual compositions would extend not only the accepted stability domain of perovskites but also open the way for a series of applications enabled by their rich physics.