cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:mp-am
title: Controlling the quantum spin Hall edge states in two-dimensional transition metal dichalcogenides

description: Two-dimensional transition metal dichalcogenides (TMDs) of Mo and W in their 1T′ crystalline phase host the quantum spin Hall (QSH) insulator phase. We address the electronic properties of the QSH edge states by means of first-principles calculations performed on realistic models of edge terminations of different stoichiometries. The QSH edge states show a tendency to have complex band dispersions and coexist with topologically trivial edge states. We nevertheless identify two stable edge terminations that allow isolation of a pair of helical edge states within the band gap of TMDs, with monolayer 1T′-WSe2 being the most promising material. We also characterize the finite-size effects in the electronic structure of 1T′-WSe2 nanoribbons. Our results provide guidance to the experimental studies and possible practical applications of QSH edge states in monolayer 1T′-TMDs.
DOI: 10.24435/materialscloud:2018.0019/v1
title: Special quasi-random structures for the 6-component high entropy alloys

description: We propose a general method to calculate the average misfit volumes of atoms in any random alloy via DFT calculations. The method is validated with an example of a 6-component equi-composition high entropy alloy. The special quasi-random structures (SQSs) used in our work are reported here.
DOI: 10.24435/materialscloud:ap-w8
title: Charge separation and charge carrier mobility in photocatalytic metal-organic frameworks

description: Metal-Organic Frameworks (MOFs) are highly versatile materials owing to their vast structural and chemical tunability. These hybrid inorganic-organic crystalline materials offer an ideal platform to incorporate light-harvesting and catalytic centers and thus, exhibit a great potential to be exploited in solar-driven photocatalytic processes such as H2 production and CO2 reduction. To be photocatalytically active, UV-visible optical absorption and appropriate band alignment with respect to the target redox potential is required. Despite fulfilling these criteria, the photocatalytic performance of MOFs is still limited by their ability to produce long-lived electron-hole pairs and long-range charge transport. In this work, we present a computational strategy to address these two descriptors in MOF structures and translate them into charge transfer numbers and effective mass values. We apply our approach to 15 MOF structures from the literature that encompass the main strategies used in the design of efficient photocatalysts including different metals, ligands, and topologies. Our results capture the main characteristics previously reported for these MOFs and enable us to identify promising candidates. In the quest of novel photocatalytic systems, high-throughput screening based on charge separation and charge mobility features is envisioned to be applied in large databases of both experimentally and in silico generated MOFs.
DOI: 10.24435/materialscloud:v5-rk
title: Special quasi-random structures for the 6-component high entropy alloys

description: We propose a general method to calculate the average misfit volumes of atoms in any random alloy via DFT calculations. The method is validated with an example of a 6-component equi-composition high entropy alloy. The special quasi-random structures (SQSs) used in our work are reported here.
DOI: 10.24435/materialscloud:ya-y0
title: Charge separation and charge carrier mobility in photocatalytic metal-organic frameworks

description: Metal-Organic Frameworks (MOFs) are highly versatile materials owing to their vast structural and chemical tunability. These hybrid inorganic-organic crystalline materials offer an ideal platform to incorporate light-harvesting and catalytic centers and thus, exhibit a great potential to be exploited in solar-driven photocatalytic processes such as H2 production and CO2 reduction. To be photocatalytically active, UV-visible optical absorption and appropriate band alignment with respect to the target redox potential is required. Despite fulfilling these criteria, the photocatalytic performance of MOFs is still limited by their ability to produce long-lived electron-hole pairs and long-range charge transport. In this work, we present a computational strategy to address these two descriptors in MOF structures and translate them into charge transfer numbers and effective mass values. We apply our approach to 15 MOF structures from the literature that encompass the main strategies used in the design of efficient photocatalysts including different metals, ligands, and topologies. Our results capture the main characteristics previously reported for these MOFs and enable us to identify promising candidates. In the quest of novel photocatalytic systems, high-throughput screening based on charge separation and charge mobility features is envisioned to be applied in large databases of both experimentally and in silico generated MOFs.
DOI: 10.24435/materialscloud:42-fm
title: Building a consistent and reproducible database for adsorption evaluation in Covalent-Organic Frameworks

description: We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent−organic frameworks (COFs) reported in the literature, to characterize their CO2 adsorption properties using the following steps: 
(1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory, 
(2) fitting atomic point charges based on the electron density, 
(3) characterizing the pore geometry of the structures before and after optimization, 
(4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally, 
(5) assessing the CO2 parasitic energy via process modeling. 

The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

*** UPDATE December 2019 ***
 - Database extended to  include 417 COFs (from papers published until September 1st 2019)
- Migration to AiiDA-v1.0.0
- Using the publicly available plugin aiida-lsmo

*** UPDATE February 2020 ***
 - Database extended to  include 505 COFs (from papers published until February 1st 2020)
 - Including AiiDA Groups for quick interactive visualization

*** UPDATE June 2020 ***
 - Database extended to include 574 COFs (from papers published until June 1st 2020)

*** UPDATE September 2020 ***
 - Include other applications than CCS, considering the same set of 574 COFs

*** UPDATE October 2020 ***
 - Database extended to include 626 COFs (from papers published until October 1st 2020)
DOI: 10.24435/materialscloud:dd-nz
title: Automated high-throughput Wannierisation

description: Maximally-localised Wannier functions (MLWFs) are routinely used to compute from first-principles advanced materials properties that require very dense Brillouin zone integration and to build accurate tight-binding models for scale-bridging simulations. At the same time, high-throughput (HT) computational materials design is an emergent field that promises to accelerate the reliable and cost-effective design and optimisation of new materials with target properties. The use of MLWFs in HT workflows has been hampered by the fact that generating MLWFs automatically and robustly without any user intervention and for arbitrary materials is, in general, very challenging. We address this problem directly by proposing a procedure for automatically generating MLWFs for HT frameworks. Our approach is based on the selected columns of the density matrix method (SCDM) and we present the details of its implementation in an AiiDA workflow. We apply our approach to a dataset of 200 bulk crystalline materials that span a wide structural and chemical space. We assess the quality of our MLWFs in terms of the accuracy of the band-structure interpolation that they provide as compared to the band-structure obtained via full first-principles calculations. We provide here an AiiDA export file with the full provenance of all simulations run in the project. Moreover, we provide a downloadable virtual machine that allows to reproduce the results of this paper and also to run new calculations for different materials, including all first-principles and atomistic simulations and the computational workflows.
DOI: 10.24435/materialscloud:2019.0043/v1
title: Amphiphilic Peptide Binding on Crystalline vs. Amorphous Silica from Molecular Dynamics Simulations

description: The leucine-lysine amphiphilic peptide LKα14 has been used to study fundamental driving forces in processes such as peptide-surface binding and biomineralization. Here, we employ molecular dynamics (MD) simulations in tandem with replica exchange metadynamics to probe the binding mechanism and thermodynamics of LKα14 on silica. We also investigate the effect that the nature of the silica surface – crystalline vs. amorphous, has on the binding properties and peptide-surface conformations. We find that water adsorbs differently on both surfaces; it forms a denser interfacial layer on the crystalline surface, compared to the amorphous surface. This causes the peptide to bind more strongly on the amorphous surface than the crystalline surface. Cluster analysis shows that the peptide adopts a helical conformation at both surfaces, with a greater distribution of states on the crystalline surface. Peptide binding is primarily through lysine interactions, in line with prior experimental results.
DOI: 10.24435/materialscloud:4j-0t
title: Amphiphilic Peptide Binding on Crystalline vs. Amorphous Silica from Molecular Dynamics Simulations

description: The leucine-lysine amphiphilic peptide LKα14 has been used to study fundamental driving forces in processes such as peptide-surface binding and biomineralization. Here, we employ molecular dynamics (MD) simulations in tandem with replica exchange metadynamics to probe the binding mechanism and thermodynamics of LKα14 on silica. We also investigate the effect that the nature of the silica surface – crystalline vs. amorphous, has on the binding properties and peptide-surface conformations. We find that water adsorbs differently on both surfaces; it forms a denser interfacial layer on the crystalline surface, compared to the amorphous surface. This causes the peptide to bind more strongly on the amorphous surface than the crystalline surface. Cluster analysis shows that the peptide adopts a helical conformation at both surfaces, with a greater distribution of states on the crystalline surface. Peptide binding is primarily through lysine interactions, in line with prior experimental results.
DOI: 10.24435/materialscloud:2019.0090/v1
title: Incorporating long-range physics in atomic-scale machine learning

description: The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a bulk material are written as a sum over contributions that depend on the configurations within finite atom-centered environments. The obvious downside of this approach is that it cannot capture nonlocal, nonadditive effects such as those arising due to long-range electrostatics or quantum interference. We propose a solution to this problem by introducing nonlocal representations of the system, which are remapped as feature vectors that are defined locally and are equivariant in O(3). We consider, in particular, one form that has the same asymptotic behavior as the electrostatic potential. We demonstrate that this framework can capture nonlocal, long-range physics by building a model for the electrostatic energy of randomly distributed point-charges, for the unrelaxed binding curves of charged organic molecular dimers, and for the electronic dielectric response of liquid water. By combining a representation of the system that is sensitive to long-range correlations with the transferability of an atom-centered additive model, this method outperforms current state-of-the-art machine-learning schemes and provides a conceptual framework to incorporate nonlocal physics into atomistic machine learning.
DOI: 10.24435/materialscloud:xy-sp
title: Incorporating long-range physics in atomic-scale machine learning

description: The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a bulk material are written as a sum over contributions that depend on the configurations within finite atom-centered environments. The obvious downside of this approach is that it cannot capture nonlocal, nonadditive effects such as those arising due to long-range electrostatics or quantum interference. We propose a solution to this problem by introducing nonlocal representations of the system, which are remapped as feature vectors that are defined locally and are equivariant in O(3). We consider, in particular, one form that has the same asymptotic behavior as the electrostatic potential. We demonstrate that this framework can capture nonlocal, long-range physics by building a model for the electrostatic energy of randomly distributed point-charges, for the unrelaxed binding curves of charged organic molecular dimers, and for the electronic dielectric response of liquid water. By combining a representation of the system that is sensitive to long-range correlations with the transferability of an atom-centered additive model, this method outperforms current state-of-the-art machine-learning schemes and provides a conceptual framework to incorporate nonlocal physics into atomistic machine learning.
DOI: 10.24435/materialscloud:cz-kq
title: Building a consistent and reproducible database for adsorption evaluation in Covalent-Organic Frameworks

description: We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent−organic frameworks (COFs) reported in the literature, to characterize their CO2 adsorption properties using the following steps: 
(1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory, 
(2) fitting atomic point charges based on the electron density, 
(3) characterizing the pore geometry of the structures before and after optimization, 
(4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally, 
(5) assessing the CO2 parasitic energy via process modeling. 

The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

*** UPDATE December 2019 ***
 - Database extended to  include 417 COFs (from papers published until September 1st 2019)
- Migration to AiiDA-v1.0.0
- Using the publicly available plugin aiida-lsmo

*** UPDATE February 2020 ***
 - Database extended to  include 505 COFs (from papers published until February 1st 2020)
 - Including AiiDA Groups for quick interactive visualization

*** UPDATE June 2020 ***
 - Database extended to include 574 COFs (from papers published until June 1st 2020)

*** UPDATE September 2020 ***
 - Include other applications than CCS, considering the same set of 574 COFs

*** UPDATE October 2020 ***
 - Database extended to include 626 COFs (from papers published until October 1st 2020)

*** UPDATE February 2021 ***
 - Database extended to include 632 COFs (from papers published until February 1st 2021). We added a total of 61 new structures but we discarded 55: see CURATED-COFs GitHub for more details.

*** UPDATE June 2021 ***
 - Database extended to include 648 valid COFs (from papers published until June 1st 2021) plus 80 discarded: see CURATED-COFs GitHub for more details.

*** UPDATE July 2022 ***
 - Database extended to include 871 valid COFs (from papers published until July 1st 2022) plus 110 discarded: see CURATED-COFs GitHub for more details.
DOI: 10.24435/materialscloud:2019.0048/v1
title: Defect Formation Energies of Interstitial C, Si, and Ge Impurities in β-Ga2O3

description: We investigate formation energies of C, Si, and Ge defects in β-Ga2O3 through hybrid functional calculations. We find that the interstitial defects of these elements generally occur at higher energies than their substitutional counterparts, while they are more stable at low Fermi energies in Ga-rich conditions. In n-type and Ga-rich conditions, interstitials of Si and Ge show significantly higher formation energies than their substitutional form, but this difference is less pronounced for C. Charge transition levels of interstitial defects lie in the upper part of the band-gap, and account for several measured levels in unintentionally doped and Ge-doped samples of β-Ga2O3.
DOI: 10.24435/materialscloud:7m-e3
title: Defect Formation Energies of Interstitial C, Si, and Ge Impurities in β-Ga2O3

description: We investigate formation energies of C, Si, and Ge defects in β-Ga2O3 through hybrid functional calculations. We find that the interstitial defects of these elements generally occur at higher energies than their substitutional counterparts, while they are more stable at low Fermi energies in Ga-rich conditions. In n-type and Ga-rich conditions, interstitials of Si and Ge show significantly higher formation energies than their substitutional form, but this difference is less pronounced for C. Charge transition levels of interstitial defects lie in the upper part of the band-gap, and account for several measured levels in unintentionally doped and Ge-doped samples of β-Ga2O3.
DOI: 10.24435/materialscloud:90-b6
title: Engineering  frustrated lewis pair active sites in porous organic scaffolds for catalytic CO₂ hydrogenation

description: Frustrated Lewis pairs (FLPs), featuring reactive combinations of Lewis acids and Lewis bases, have been utilized for myriad metal-free homogeneous catalytic processes. Immobilizing the active Lewis sites to a solid support, especially to porous scaffolds, has shown great potential to ameliorate FLP catalysis by circumventing some of its inherent drawbacks, such as product separation and catalyst recyclability. Nevertheless, designing immobilized Lewis pair active sites (LPASs) is challenging due to the requirement of placing the donor and acceptor centers in appropriate geometric arrangements while maintaining the necessary chemical environment to perform catalysis, and clear design rules have not yet been established. In this work, we formulate simple guidelines to build highly active LPASs for direct catalytic hydrogenation of CO₂ through a large-scale screening of a diverse library of 25,000 immobilized FLPs. 
The library is built by introducing boron-containing acidic sites in the vicinity of the existing basic nitrogen sites of the organic linkers of metal-organic frameworks collected in a "top-down" fashion from an experimental database. The chemical and geometrical appropriateness of these LPASs for CO₂ hydrogenation is determined by evaluating a series of simple descriptors representing the intrinsic strength (acidity and basicity) of the components and their spatial arrangement in the active sites. Analysis of the leading candidates enables the formulation of pragmatic and experimentally-relevant design principles and they constitute the starting point for further exploration of FLP-based catalysts for the reduction of CO₂.
DOI: 10.24435/materialscloud:5q-jt
title: Building a consistent and reproducible database for adsorption evaluation in Covalent-Organic Frameworks

description: We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent−organic frameworks (COFs) reported in the literature, to characterize their CO2 adsorption properties using the following steps: 
(1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory, 
(2) fitting atomic point charges based on the electron density, 
(3) characterizing the pore geometry of the structures before and after optimization, 
(4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally, 
(5) assessing the CO2 parasitic energy via process modeling. 

The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

*** UPDATE December 2019 ***
 - Database extended to  include 417 COFs (from papers published until September 1st 2019)
- Migration to AiiDA-v1.0.0
- Using the publicly available plugin aiida-lsmo

*** UPDATE February 2020 ***
 - Database extended to  include 505 COFs (from papers published until February 1st 2020)
 - Including AiiDA Groups for quick interactive visualization

*** UPDATE June 2020 ***
 - Database extended to include 574 COFs (from papers published until June 1st 2020)

*** UPDATE September 2020 ***
 - Include other applications than CCS, considering the same set of 574 COFs

*** UPDATE October 2020 ***
 - Database extended to include 626 COFs (from papers published until October 1st 2020)

*** UPDATE February 2021 ***
 - Database extended to include 632 COFs (from papers published until February 1st 2021). We added a total of 61 new structures but we discarded 55: see CURATED-COFs GitHub for more details.