cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:pn-hh
title: Properties of α-brass nanoparticles. 1. Neural network potential energy surface

description: **Data for Properties of α-Brass Nanoparticles. 1. Neural Network Potential Energy Surface**
Jan Weinreich, Anton Römer, Martín Leandro Paleico, and Jörg Behler

53 841 reference structures of alpha brass (less 40 % Zn) with following split
 - 4009 brass clusters
 - 8492 molten brass bulk structures
 - 8964 copper slabs, and 16 878 brass slabs
 - 5377 copper bulk structures
 - 10 121 brass bulk structures have been included. 

53 841 total energies and 8 903 340 force components. The ranges of values for the energies and force components to be fitted have a width of about 2 eV/atom and 15 eV/Å, respectively. However, some structures may have slightly higher Zn content as discussed in Fig 3 (https://arxiv.org/abs/2001.10906)

The archive contains an easily usable npz file as well as the original input.data file used to fit the potential energy surface. Additionally a Jupyter notebook describes in great detail how the data was converted to the npz format and how to read the data e.g. for subsequent use with python. In addition an example VASP calculation was added to provide detailed information about how the reference data was calculated with DFT.

DETAILS:
- DFT PB VASP-5.3 target accuracy of the total energy few meV/atom
- Convergence tests with respect to the number of k-points showed that in order to fulfill this criterion a k-point grid of 12 × 12 × 12 is needed for a conventional four-atom copper fcc unit cell with a lattice constant of about 3.63 Å along with a plane wave cutoff energy of 500 eV and projector augmented wave potentials.(61,63)
- Larger systems have been calculated using an adapted k-point grid corresponding to the same k-point density. The Γ-point centered k-point grids have been constructed employing the Monkhorst–Pack scheme. For surface calculations, 4–14 layer slabs with a total vacuum thickness of at least 8 Å have been used. 
- In the case of cluster calculations, which have also been treated in a periodic setup, the periodic images of the clusters have been separated by at least 8 Å in all three spatial directions. The convergence of very large clusters with diameters of d ≈ 22 Å, which have been used to include specific atomic environments in the data set, has been extensively tested and we found that using the Γ-point only is sufficient to reach the required convergence level.
DOI: 10.24435/materialscloud:jk-zm
title: Automatized discovery of polymer membranes with AI generative design and molecular dynamics simulations

description: Data sets and scripts for computational discovery of polymer membranes for carbon dioxide separation. The training data set with 1,169 homo-polymers provides carbon dioxide permeability, glass transition temperature and half decomposition temperature for each listed material. The output data set contains 784 optimized homo-polymer candidates generated by Inverse Design and Machine Learning techniques. The Jupyter notebook enables the use of the Polymer Property Prediction Engine as a service for generating the properties provided in the training data set.
DOI: 10.24435/materialscloud:av-cy
title: Synthesis and characterization of [7]triangulene

description: In this record we provide data to support our recent findings related to the fabrication of [7]triangulene. Triangulene and its π-extended homologues constitute non-Kekulé polyradical frameworks with high-spin ground states, and are anticipated to be key components of organic spintronic devices. In our publication we report a combined in-solution and on-surface synthesis of the hitherto largest triangulene homologue, [7]triangulene (C78H24), consisting of twenty-eight benzenoid rings fused in a triangular fashion. We employ low-temperature scanning tunneling microscopy to confirm the chemical structure of individual molecules adsorbed on a Cu(111) surface. While neutral [7]triangulene in the gas phase is predicted to have an open-shell septet ground state; our scanning tunneling spectroscopy measurements, in combination with density functional theory calculations, reveal chemisorption of [7]triangulene on Cu(111) together with considerable charge transfer, resulting in a closed-shell state. Furthermore, substantial hybridization between the molecular orbitals of [7]triangulene is observed.
DOI: 10.24435/materialscloud:1j-9n
title: Synthesis and characterization of [7]triangulene

description: In this record we provide data to support our recent findings related to the fabrication of [7]triangulene. Triangulene and its π-extended homologues constitute non-Kekulé polyradical frameworks with high-spin ground states, and are anticipated to be key components of organic spintronic devices. In our publication we report a combined in-solution and on-surface synthesis of the hitherto largest triangulene homologue, [7]triangulene (C78H24), consisting of twenty-eight benzenoid rings fused in a triangular fashion. We employ low-temperature scanning tunneling microscopy to confirm the chemical structure of individual molecules adsorbed on a Cu(111) surface. While neutral [7]triangulene in the gas phase is predicted to have an open-shell septet ground state; our scanning tunneling spectroscopy measurements, in combination with density functional theory calculations, reveal chemisorption of [7]triangulene on Cu(111) together with considerable charge transfer, resulting in a closed-shell state. Furthermore, substantial hybridization between the molecular orbitals of [7]triangulene is observed.
DOI: 10.24435/materialscloud:5j-9m
title: A new dataset of 415k stable and metastable materials calculated with the PBEsol and SCAN functionals

description: In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for an increased accuracy in the calculations. Here, we present the updated alexandria dataset of calculations for more than 415k solid-state materials obtained with two improved functionals: PBE for solids (that yields consistently better geometries than the PBE) and SCAN (probably the best all-around functional at the moment). Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for more reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBE for solids, and SCAN.
DOI: 10.24435/materialscloud:6b-31
title: A new dataset of 415k stable and metastable materials calculated with the PBEsol and SCAN functionals

description: In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for an increased accuracy in the calculations. Here, we present the updated alexandria dataset of calculations for more than 415k solid-state materials obtained with two improved functionals: PBE for solids (that yields consistently better geometries than the PBE) and SCAN (probably the best all-around functional at the moment). Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for more reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBE for solids, and SCAN.
DOI: 10.24435/materialscloud:ma-qn
title: AI powered, automated discovery of polymer membranes for carbon capture

description: Data sets and scripts for computational discovery of polymer membranes for carbon dioxide separation. The training data set with 1,169 homo-polymers provides carbon dioxide permeability, glass transition temperature and half decomposition temperature for each listed material. The output data set contains 784 optimized homo-polymer candidates generated by Inverse Design and Machine Learning techniques. The Jupyter notebook enables the use of the Polymer Property Prediction Engine as a service for generating the properties provided in the training data set.
DOI: 10.24435/materialscloud:sw-20
title: First principles study of the effect of hydrogen in austenitic stainless steels and high entropy alloys

description: Hydrogen (H) embrittlement in multicomponent austenitic alloys is a serious limitation to their application in many environments. Recent experiments show that the High-Entropy Alloy (HEA) CoCrFeMnNi absorbs more H than 304 Stainless Steel but is less prone to embrittlement while the HEA CoCrFeNi is not embrittled under comparable conditions. As a first step toward understanding H embrittlement, here a comprehensive first-principles study of H absorption, surface, and fracture energies in the presence of H is presented for 304 Stainless Steel, 316 Stainless Steel, CoCrFeNi, and CoCrFeMnNi. A collinear paramagnetic model of the magnetic state is used, which is likely more realistic than previous proposed magnetic states. All alloys have a statistical distribution of H absorption sites. Hence, at low concentrations, H is effectively trapped in the lattice making it more difficult for H to segregate to defects or interfaces. Agreement with experimental H solubilities across a range of chemical potentials can be achieved with minor fitting of the average H absorption energy. The (111) surface energies for 0, 50, and 100% H surface coverage are very similar across all alloys. The fracture energies for two representative thermodynamic conditions are then determined. SS304 and CoCrFeNi are found to have the lowest fracture energies, but experiments suggest rather different embrittlement tendencies. These results indicate that differences in H embrittlement across these austenitic alloys are not due solely to differences in H absorption or H-reduced fracture energy, thus requiring more sophisticated concepts than those recently found successful for fcc Ni.
DOI: 10.24435/materialscloud:2p-e8
title: Quantifying photoinduced polaronic distortions in inorganic lead halide perovskites nanocrystals

description: The development of next generation perovskite-based optoelectronic devices relies critically on the understanding of the interaction between charge carriers and the polar lattice in out-of-equilibrium conditions. While it has become increasingly evident for CsPbBr3 perovskites that the Pb-Br framework flexibility plays a key role in their light-activated functionality, the corresponding local structural rearrangement has not yet been unambiguously identified. In this work, we demonstrate that the photoinduced lattice changes in the system are due to a specific polaronic distortion, associated with the activation of a longitudinal optical phonon mode at 18 meV by electron-phonon coupling, and we quantify the associated structural changes with atomic-level precision. Key to this achievement is the combination of time-resolved and temperature-dependent studies at Br K-edge and Pb L3-edge X-ray absorption with refined ab-initio simulations, which fully account for the screened core-hole final state effects on the X-ray absorption spectra. From the temporal kinetics, we show that carrier recombination reversibly unlocks the structural deformation at both Br and Pb sites. The comparison with the temperature-dependent XAS results rules out thermal effects as the primary source of distortion of the Pb-Br bonding motif during photoexcitation. Our work provides a comprehensive description of the CsPbBr3 perovskites photophysics, offering novel insights on the light-induced response of the system and its exceptional optoelectronic properties.
DOI: 10.24435/materialscloud:sa-jy
title: First principles study of the effect of hydrogen in austenitic stainless steels and high entropy alloys

description: Hydrogen (H) embrittlement in multicomponent austenitic alloys is a serious limitation to their application in many environments. Recent experiments show that the High-Entropy Alloy (HEA) CoCrFeMnNi absorbs more H than 304 Stainless Steel but is less prone to embrittlement while the HEA CoCrFeNi is not embrittled under comparable conditions. As a first step toward understanding H embrittlement, here a comprehensive first-principles study of H absorption, surface, and fracture energies in the presence of H is presented for 304 Stainless Steel, 316 Stainless Steel, CoCrFeNi, and CoCrFeMnNi. A collinear paramagnetic model of the magnetic state is used, which is likely more realistic than previous proposed magnetic states. All alloys have a statistical distribution of H absorption sites. Hence, at low concentrations, H is effectively trapped in the lattice making it more difficult for H to segregate to defects or interfaces. Agreement with experimental H solubilities across a range of chemical potentials can be achieved with minor fitting of the average H absorption energy. The (111) surface energies for 0, 50, and 100% H surface coverage are very similar across all alloys. The fracture energies for two representative thermodynamic conditions are then determined. SS304 and CoCrFeNi are found to have the lowest fracture energies, but experiments suggest rather different embrittlement tendencies. These results indicate that differences in H embrittlement across these austenitic alloys are not due solely to differences in H absorption or H-reduced fracture energy, thus requiring more sophisticated concepts than those recently found successful for fcc Ni.
DOI: 10.24435/materialscloud:ch-j7
title: Quantifying photoinduced polaronic distortions in inorganic lead halide perovskites nanocrystals

description: The development of next generation perovskite-based optoelectronic devices relies critically on the understanding of the interaction between charge carriers and the polar lattice in out-of-equilibrium conditions. While it has become increasingly evident for CsPbBr3 perovskites that the Pb-Br framework flexibility plays a key role in their light-activated functionality, the corresponding local structural rearrangement has not yet been unambiguously identified. In this work, we demonstrate that the photoinduced lattice changes in the system are due to a specific polaronic distortion, associated with the activation of a longitudinal optical phonon mode at 18 meV by electron-phonon coupling, and we quantify the associated structural changes with atomic-level precision. Key to this achievement is the combination of time-resolved and temperature-dependent studies at Br K-edge and Pb L3-edge X-ray absorption with refined ab-initio simulations, which fully account for the screened core-hole final state effects on the X-ray absorption spectra. From the temporal kinetics, we show that carrier recombination reversibly unlocks the structural deformation at both Br and Pb sites. The comparison with the temperature-dependent XAS results rules out thermal effects as the primary source of distortion of the Pb-Br bonding motif during photoexcitation. Our work provides a comprehensive description of the CsPbBr3 perovskites photophysics, offering novel insights on the light-induced response of the system and its exceptional optoelectronic properties.
DOI: 10.24435/materialscloud:3r-gn
title: Elucidating the structure-dependent selectivity towards methane and ethanol of CuZn in the CO2 electroreduction using tailored Cu/ZnO precatalysts

description: Understanding the catalyst compositional and structural features that control selectivity is of uttermost importance to target desired products in chemical reactions. In this joint experimental–computational work, we leverage tailored Cu/ZnO precatalysts as a material platform to identify the intrinsic features of methane-producing and ethanol-producing CuZn catalysts in the electrochemical CO2 reduction reaction (CO2RR). Specifically, we find that Cu@ZnO nanocrystals, where a central Cu domain is decorated with ZnO domains, and ZnO@Cu nanocrystals, where a central ZnO domain is decorated with Cu domains, evolve into Cu@CuZn core@shell catalysts that are selective for methane (∼52%) and ethanol (∼39%), respectively. Operando X-ray absorption spectroscopy and various microscopy methods evidence that a higher degree of surface alloying along with a higher concentration of metallic Zn improve the ethanol selectivity. Density functional theory explains that the combination of electronic and tandem effects accounts for such selectivity. These findings mark a step ahead towards understanding structure–property relationships in bimetallic catalysts for the CO2RR and their rational tuning to increase selectivity towards target products, especially alcohols.
DOI: 10.24435/materialscloud:k1-h4
title: Elucidating the structure-dependent selectivity towards methane and ethanol of CuZn in the CO2 electroreduction using tailored Cu/ZnO precatalysts

description: Understanding the catalyst compositional and structural features that control selectivity is of uttermost importance to target desired products in chemical reactions. In this joint experimental–computational work, we leverage tailored Cu/ZnO precatalysts as a material platform to identify the intrinsic features of methane-producing and ethanol-producing CuZn catalysts in the electrochemical CO2 reduction reaction (CO2RR). Specifically, we find that Cu@ZnO nanocrystals, where a central Cu domain is decorated with ZnO domains, and ZnO@Cu nanocrystals, where a central ZnO domain is decorated with Cu domains, evolve into Cu@CuZn core@shell catalysts that are selective for methane (∼52%) and ethanol (∼39%), respectively. Operando X-ray absorption spectroscopy and various microscopy methods evidence that a higher degree of surface alloying along with a higher concentration of metallic Zn improve the ethanol selectivity. Density functional theory explains that the combination of electronic and tandem effects accounts for such selectivity. These findings mark a step ahead towards understanding structure–property relationships in bimetallic catalysts for the CO2RR and their rational tuning to increase selectivity towards target products, especially alcohols.
DOI: 10.24435/materialscloud:ma-eq
title: ML powered, automated discovery of polymer membranes for carbon capture

description: Data sets and scripts for computational discovery of polymer membranes for carbon dioxide separation. The training data set with 1,169 homo-polymers provides carbon dioxide permeability, glass transition temperature and half decomposition temperature for each listed material. The output data set contains 784 optimized homo-polymer candidates generated by Inverse Design and Machine Learning techniques. The Jupyter notebook enables the use of the Polymer Property Prediction Engine as a service for generating the properties provided in the training data set.
DOI: 10.24435/materialscloud:em-r4
title: High-throughput activity screening and sorting of single catalyst particles with a droplet microreactor using dielectrophoresis

description: This dataset, consisting of 29 videos, has been used for the publication 'High-Throughput Activity Screening and Sorting of Single Catalyst Particles with a Droplet Microreactor Using Dielectrophoresis': "A droplet microreactor has been developed for fluorescence activated sorting of catalyst particles using dielectrophoresis. Fluid Catalytic Cracking (FCC) particles stained with styrene derivatives are analyzed with the developed analytical platform and sorted based on catalytic activity. Highly active and low-to-moderately active catalyst particles are sorted out, using 4-fluorostyrene or 4-methoxystyrene as a probe respectively. The FCC particles are encapsulated in liquid droplets, where the fluorescent FCC particles activate the dielectrophoretic sorter, and are sorted within 200 ms."
DOI: 10.24435/materialscloud:ae-dz
title: High-throughput activity screening and sorting of single catalyst particles with a droplet microreactor using dielectrophoresis

description: This dataset, consisting of 29 videos, has been used for the publication 'High-Throughput Activity Screening and Sorting of Single Catalyst Particles with a Droplet Microreactor Using Dielectrophoresis': "A droplet microreactor has been developed for fluorescence activated sorting of catalyst particles using dielectrophoresis. Fluid Catalytic Cracking (FCC) particles stained with styrene derivatives are analyzed with the developed analytical platform and sorted based on catalytic activity. Highly active and low-to-moderately active catalyst particles are sorted out, using 4-fluorostyrene or 4-methoxystyrene as a probe respectively. The FCC particles are encapsulated in liquid droplets, where the fluorescent FCC particles activate the dielectrophoretic sorter, and are sorted within 200 ms."
DOI: 10.24435/materialscloud:64-c8
title: AI powered, automated discovery of polymer membranes for carbon capture

description: Data sets and scripts for computational discovery of polymer membranes for carbon dioxide separation. The training data set with 1,169 homo-polymers provides carbon dioxide permeability, glass transition temperature and half decomposition temperature for each listed material. The output data set contains 784 optimized homo-polymer candidates generated by Inverse Design and Machine Learning techniques. The Jupyter notebook enables the use of the Polymer Property Prediction Engine as a service for generating the properties provided in the training data set.
DOI: 10.24435/materialscloud:b3-5s
title: AI powered, automated discovery of polymer membranes for carbon capture

description: Data sets and scripts for computational discovery of polymer membranes for carbon dioxide separation. The training data set with 1,169 homo-polymers provides carbon dioxide permeability, glass transition temperature and half decomposition temperature for each listed material. The output data set contains 784 optimized homo-polymer candidates generated by Inverse Design and Machine Learning techniques. The Jupyter notebook enables the use of the Polymer Property Prediction Engine as a service for generating the properties provided in the training data set.
DOI: 10.24435/materialscloud:tm-js
title: Computational synthesis of substrates by crystal cleavage

description: In order to identify novel substrate materials, we developed a high-throughput bond breaking algorithm. This algorithm takes a three-dimensional crystal as input, systematically breaks bonds, and checks if the bonding network has been reduced to two periodic directions. We apply this algorithm to Materials Project database and identify 4,693 symmetrically unique cleaved surfaces across 2,133 crystals. We then characterize the thermodynamic stability of these cleaved surfaces using the DFT software VASP, characterizing 3,991 surfaces as potential substrates with energy comparable to the experimentally used substrates (0001) AlN, ZnO, and CdS.

This repository contains the structure files, setting files, pseudopotential choices, bulk precursor structure and MaterialsProject ID, and thermodynamic data for the substrates considered in this work.