100+ datasets found
  1. e

    The Materials Cloud 2D database (MC2D) - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Oct 23, 2023
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    (2023). The Materials Cloud 2D database (MC2D) - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/eacb138d-4f26-5971-a5ad-82a15c5abb36
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    Dataset updated
    Oct 23, 2023
    Description

    Two-dimensional (2D) materials are among the most promising candidates for beyond silicon electronic and optoelectronic applications. Recently, their recognized importance, sparked a race to discover and characterize new 2D materials. Within few years the number of experimentally exfoliated or synthesized 2D materials went from a couple of dozens to few hundreds while the number theoretically predicted compounds reached a few thousands. In 2018 we first contributed to this effort with the identification of 1825 compounds that are either easily (1036) or potentially (789) exfoliable from experimentally known 3D compounds. In the present work we report on the new materials recently added to the 2D-portfolio thanks to the extension of the screening to an additional experimental database (MPDS) as well as the most up-to-date versions of the two databases (ICSD and COD) used in our previous work. This expansion led to the discovery of an additional 1252 unique monolayers bringing the total to 3077 compounds and, notably, almost doubling the number of easily exfoliable materials (2004). Moreover, we optimized the structural properties of all the materials (regardless of their binding energy or number of atoms in the unit cell) as isolated mono-layer and explored their electronic band structure. This archive entry contains the database of 2D materials in particular it contains the structural parameters for all the 3077 structures of the global Material Cloud 2D database as extracted from their bulk 3D parent, 2710 optimized 2D structures and 2345 electronic band structure together with the provenance of all data and calculations as stored by AiiDA.

  2. e

    Materials Cloud three-dimensional crystals database (MC3D) - Dataset -...

    • b2find.eudat.eu
    Updated Oct 22, 2023
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    (2023). Materials Cloud three-dimensional crystals database (MC3D) - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/3c283b29-41b2-54d8-86a3-5cfd65e1de24
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    Dataset updated
    Oct 22, 2023
    Description

    The Materials Cloud three-dimensional database is a curated set of relaxed three-dimensional crystal structures based on raw CIF data taken from the external experimental databases MPDS, COD and ICSD. The raw CIF data have been imported, cleaned and parsed into a crystal structure; their ground-state has been computed using the SIRIUS-enabled pw.x code of the Quantum ESPRESSO distribution, and tight tolerance criteria for the calculations using the SSSP protocols. This entire procedure is encoded into an AiiDA workflow which automates the process while keeping full data provenance. Here, since the original source data of the ICSD and MPDS databases are copyrighted, only the provenance of the final SCF calculation on the relaxed structures can be made publicly available. The MC3D ID numbers come from a list of unique "parent" stoichiometric structures that has been created and curated from a collection of these experimental databases. Once a parent structure has been optimized using density-functional theory, it is made public and added to the online Discover section of the Materials Cloud (as mentioned, copyright might prevent publishing the original parent). Note that since not all structures have been calculated, some ID numbers are missing from the public version of the database. The full ID of each structure also contains as an appended modifier the functional that was used in the calculations. Since the ID number points to the same unique parent, mc3d-1234/pbe and mc3d-1234/pbesol have the same starting point, but have been then relaxed according to their respective functionals.

  3. m

    Data from: Unraveling the effects of inter-site Hubbard interactions in...

    • archive.materialscloud.org
    tar, text/markdown +1
    Updated Feb 13, 2023
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    Iurii Timrov; Michele Kotiuga; Nicola Marzari; Iurii Timrov; Michele Kotiuga; Nicola Marzari (2023). Unraveling the effects of inter-site Hubbard interactions in spinel Li-ion cathode materials [Dataset]. http://doi.org/10.24435/materialscloud:ry-v5
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    text/markdown, tar, txtAvailable download formats
    Dataset updated
    Feb 13, 2023
    Dataset provided by
    Materials Cloud
    Authors
    Iurii Timrov; Michele Kotiuga; Nicola Marzari; Iurii Timrov; Michele Kotiuga; Nicola Marzari
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Accurate first-principles predictions of the structural, electronic, magnetic, and electrochemical properties of cathode materials can be key in the design of novel efficient Li-ion batteries. Spinel-type cathode materials LixMn2O4 and LixMn1.5Ni0.5O4 are promising candidates for Li-ion battery technologies, but they present serious challenges when it comes to their first-principles modeling. Here, we use density-functional theory with extended Hubbard functionals - DFT+U+V with on-site U and inter-site V Hubbard interactions - to study the properties of these transition-metal oxides. The Hubbard parameters are computed from first-principles using density-functional perturbation theory. We show that while U is crucial to obtain the right trends in properties of these materials, V is essential for a quantitative description of the structural and electronic properties, as well as the Li-intercalation voltages. This work paves the way for reliable first-principles studies of other families of cathode materials without relying on empirical fitting or calibration procedures.

  4. m

    Data from: Large-scale machine-learning-assisted exploration of the whole...

    • archive.materialscloud.org
    bz2, text/markdown +2
    Updated Oct 4, 2022
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    Jonathan Schmidt; Noah Hoffmann; Hai-Chen Wang; Pedro Borlido; Pedro J. M.A. Carriço; Tiago F. T. Cerqueira; Silvana Botti; Miguel A. L. Marques; Jonathan Schmidt; Noah Hoffmann; Hai-Chen Wang; Pedro Borlido; Pedro J. M.A. Carriço; Tiago F. T. Cerqueira; Silvana Botti; Miguel A. L. Marques (2022). Large-scale machine-learning-assisted exploration of the whole materials space [Dataset]. http://doi.org/10.24435/materialscloud:m7-50
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    bz2, txt, text/markdown, text/x-pythonAvailable download formats
    Dataset updated
    Oct 4, 2022
    Dataset provided by
    Materials Cloud
    Authors
    Jonathan Schmidt; Noah Hoffmann; Hai-Chen Wang; Pedro Borlido; Pedro J. M.A. Carriço; Tiago F. T. Cerqueira; Silvana Botti; Miguel A. L. Marques; Jonathan Schmidt; Noah Hoffmann; Hai-Chen Wang; Pedro Borlido; Pedro J. M.A. Carriço; Tiago F. T. Cerqueira; Silvana Botti; Miguel A. L. Marques
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials exhibited, however, strong biases originating from underrepresented chemical elements and structural prototypes in the available data. We tackled this issue computing additional data to provide better balance across both chemical and crystal-symmetry space. Crystal-graph networks trained with this new data show unprecedented generalization accuracy, and allow for reliable, accelerated exploration of the whole space of inorganic compounds. We applied this universal network to performed machine-learning assisted high-throughput materials searches including 2500 binary and ternary prototypes and spanning about 1 billion compounds. After validation using density-functional theory, we uncover in total 19512 additional materials on the convex hull of thermodynamic stability and around 150000 compounds with a distance of less than 50 meV/atom from the hull. Here we include the DCGAT-1, DCGAT-2, and DCGAT-3 datasets used in this work.

  5. m

    Zeo-1: A computational data set of zeolite structures

    • archive.materialscloud.org
    application/gzip +1
    Updated Oct 27, 2021
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    Leonid Komissarov; Toon Verstraelen; Leonid Komissarov; Toon Verstraelen (2021). Zeo-1: A computational data set of zeolite structures [Dataset]. http://doi.org/10.24435/materialscloud:cv-zd
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    text/markdown, application/gzipAvailable download formats
    Dataset updated
    Oct 27, 2021
    Dataset provided by
    Materials Cloud
    Authors
    Leonid Komissarov; Toon Verstraelen; Leonid Komissarov; Toon Verstraelen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Fast, empirical potentials are gaining increased popularity in the computational fields of materials science, physics and chemistry. With it, there is a rising demand for high-quality reference data for the training and validation of such models. In contrast to research that is mainly focused on small organic molecules, this work presents a data set of geometry-optimized bulk phase zeolite structures. Covering a majority of framework types from the Database of Zeolite Structures, this set includes over thirty thousand geometries. Calculated properties include system energies, nuclear gradients and stress tensors at each point, making the data suitable for model development, validation or referencing applications focused on periodic silica systems.

  6. m

    Data from: Enlisting potential cathode materials for rechargeable Ca...

    • archive.materialscloud.org
    tar, text/markdown +1
    Updated Apr 1, 2021
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    M. Elena Arroyo-de Dompablo; Jose Luis Casals; M. Elena Arroyo-de Dompablo; Jose Luis Casals (2021). Enlisting potential cathode materials for rechargeable Ca batteries. [Dataset]. http://doi.org/10.24435/materialscloud:4j-gj
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    tar, txt, text/markdownAvailable download formats
    Dataset updated
    Apr 1, 2021
    Dataset provided by
    Materials Cloud
    Authors
    M. Elena Arroyo-de Dompablo; Jose Luis Casals; M. Elena Arroyo-de Dompablo; Jose Luis Casals
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The development of rechargeable batteries based on a Ca metal anode demands the identification of suitable cathode materials. This work investigates the potential application of a variety of compounds, which are selected from the In-organic Crystal Structural Database (ICSD) considering 3d-transition metal oxysulphides, pyrophosphates, silicates, nitrides, and phosphates with a maximum of four different chemical elements in their composition. Cathode perfor-mance of CaFeSO, CaCoSO, CaNiN, Ca3MnN3, Ca2Fe(Si2O7), CaM(P2O7) (M = V, Cr, Mn, Fe, Co), CaV2(P2O7)2, Ca(VO)2(PO4)2 and α-VOPO4 is evaluated throughout the calculation of operation voltages, volume changes associated to the redox reaction and mobility of Ca2+ ions. Some materials exhibit attractive specific capacities and intercalation voltages combined with energy barriers for Ca migration around 1 eV (CaFeSO, Ca2FeSi2O7 and CaV2(P2O7)2). Based on the DFT results, αI-VOPO4 is identified as a potential Ca-cathode with a maximum theoretical specific capacity of 312 mAh/g, an average intercalation voltage of 2.8 V and calculated energy barriers for Ca migration below 0.65 eV (GGA functional).

  7. m

    Data from: Inverse design of singlet fission materials with...

    • archive.materialscloud.org
    csv, text/markdown +2
    Updated Jul 4, 2024
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    Luca Schaufelberger; J. Terence Blaskovits; Ruben Laplaza; Clemence Corminboeuf; Kjell Jorner; Luca Schaufelberger; J. Terence Blaskovits; Ruben Laplaza; Clemence Corminboeuf; Kjell Jorner (2024). Inverse design of singlet fission materials with uncertainty-controlled genetic optimization [Dataset]. http://doi.org/10.24435/materialscloud:yn-vz
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    zip, txt, text/markdown, csvAvailable download formats
    Dataset updated
    Jul 4, 2024
    Dataset provided by
    Materials Cloud
    Authors
    Luca Schaufelberger; J. Terence Blaskovits; Ruben Laplaza; Clemence Corminboeuf; Kjell Jorner; Luca Schaufelberger; J. Terence Blaskovits; Ruben Laplaza; Clemence Corminboeuf; Kjell Jorner
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Singlet fission has shown potential for boosting the power conversion efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and singlet exciton size for the discovery of singlet fission materials. We show that uncertainty in the model predictions can control how far the genetic optimization moves away from previously known molecules. Running the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom-rich mesoionic compounds as acceptors for charge-transfer mediated singlet fission. When included in larger conjugated donor-acceptor systems, these units exhibit strong localization of the triplet state, favorable diradicaloid character and suitable triplet energies for exciton injection into semiconductor solar cells. As the proposed candidates are composed of fragments from synthesized molecules, they are likely synthetically accessible.

  8. m

    The nature of the active sites on Ni/CeO2 catalysts for methane conversions

    • archive.materialscloud.org
    text/markdown, txt +1
    Updated May 21, 2021
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    Pablo G. Lustemberg; Zhongtian Mao; Agustín Salcedo; Beatriz Irigoyen; M. Verónica Ganduglia-Pirovano; Charles T. Campbell; Pablo G. Lustemberg; Zhongtian Mao; Agustín Salcedo; Beatriz Irigoyen; M. Verónica Ganduglia-Pirovano; Charles T. Campbell (2021). The nature of the active sites on Ni/CeO2 catalysts for methane conversions [Dataset]. http://doi.org/10.24435/materialscloud:ks-qb
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    zip, txt, text/markdownAvailable download formats
    Dataset updated
    May 21, 2021
    Dataset provided by
    Materials Cloud
    Authors
    Pablo G. Lustemberg; Zhongtian Mao; Agustín Salcedo; Beatriz Irigoyen; M. Verónica Ganduglia-Pirovano; Charles T. Campbell; Pablo G. Lustemberg; Zhongtian Mao; Agustín Salcedo; Beatriz Irigoyen; M. Verónica Ganduglia-Pirovano; Charles T. Campbell
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Effective catalysts for the direct conversion of methane to methanol and for methane's dry reforming to syngas are Holy Grails of catalysis research toward clean energy technologies. It has recently been discovered that Ni at low loadings on CeO2 is very reactive towards reactants CH4, H2O and CO2 and active for both of these reactions. Revealing the nature of the active sites in such systems is paramount to a rational design of improved catalysts. Here, using a combination of experimental measurements and density functional theory calculations, we show that the most active sites are cationic Ni atoms in clusters at step edges on the CeO2 surface, using the activation of CH4 as an example . We show that the size and morphology of the supported nanoparticles together with strong Ni−support bonding and charge transfer at the step edge are key to the high catalytic activity towards these methane conversions. We anticipate that this knowledge will inspire the development of more efficient catalysts for these reactions.

  9. f

    TCSP2.0_database

    • figshare.com
    application/gzip
    Updated Feb 10, 2025
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    Lai Wei (2025). TCSP2.0_database [Dataset]. http://doi.org/10.6084/m9.figshare.28379060.v1
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    application/gzipAvailable download formats
    Dataset updated
    Feb 10, 2025
    Dataset provided by
    figshare
    Authors
    Lai Wei
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    TCSP 2.0 templte database, it includes the Materials Project (MP) database, Materials Cloud database (both 2D and 3D), The Computational 2D Materials Database (C2DB), and Graph Networks for Materials Science database(GNoME).

  10. m

    Data from: Data-driven studies of magnetic two-dimensional materials

    • archive.materialscloud.org
    • materialscloud-archive-failover.cineca.it
    csv, rtf +1
    Updated May 20, 2019
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    Trevor David Rhone; Wei Chen; Shaan Desai; Amir Yacoby; Efthimios Kaxiras; Trevor David Rhone; Wei Chen; Shaan Desai; Amir Yacoby; Efthimios Kaxiras (2019). Data-driven studies of magnetic two-dimensional materials [Dataset]. http://doi.org/10.24435/materialscloud:2019.0020/v1
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    csv, text/markdown, rtfAvailable download formats
    Dataset updated
    May 20, 2019
    Dataset provided by
    Materials Cloud
    Authors
    Trevor David Rhone; Wei Chen; Shaan Desai; Amir Yacoby; Efthimios Kaxiras; Trevor David Rhone; Wei Chen; Shaan Desai; Amir Yacoby; Efthimios Kaxiras
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on the known material Cr2Ge2Te6, using density functional theory (DFT) calculations and determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability.

  11. c

    Data from: Accurate and efficient protocols for high-throughput...

    • materialscloud-archive-failover.cineca.it
    • archive.materialscloud.org
    bin, text/markdown +1
    Updated Apr 17, 2025
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    Gabriel de Miranda Nascimento; Flaviano José dos Santos; Marnik Bercx; Giovanni Pizzi; Nicola Marzari; Gabriel de Miranda Nascimento; Flaviano José dos Santos; Marnik Bercx; Giovanni Pizzi; Nicola Marzari (2025). Accurate and efficient protocols for high-throughput first-principles materials simulations [Dataset]. http://doi.org/10.24435/materialscloud:nr-hq
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    bin, zip, text/markdownAvailable download formats
    Dataset updated
    Apr 17, 2025
    Dataset provided by
    Materials Cloud
    Authors
    Gabriel de Miranda Nascimento; Flaviano José dos Santos; Marnik Bercx; Giovanni Pizzi; Nicola Marzari; Gabriel de Miranda Nascimento; Flaviano José dos Santos; Marnik Bercx; Giovanni Pizzi; Nicola Marzari
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A major challenge in first-principles high-throughput materials simulations is automating the selection of parameters used by simulation codes in a way that robustly ensures numerical precision and computational efficiency. Here, we propose a rigorous methodology to assess the quality of self-consistent DFT calculations with respect to smearing and k-point sampling across a wide range of crystalline materials. To achieve this, we develop criteria to reliably control average errors in total energies, forces, and other properties as a function of the desired computational efficiency, while consistently suppressing uncontrollable k-point sampling errors. Our results provide automated protocols for selecting optimized parameters based on different precision and efficiency tradeoffs. This archive contains all data related to the material structures and calculation workflows developed in this work.

  12. Materials Cloud, An Open Science Portal for FAIR Data Sharing

    • figshare.com
    mp4
    Updated Jun 5, 2023
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    Scientific Data; Aliaksandr V. Yakutovich (2023). Materials Cloud, An Open Science Portal for FAIR Data Sharing [Dataset]. http://doi.org/10.6084/m9.figshare.7611347.v1
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    mp4Available download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Scientific Data; Aliaksandr V. Yakutovich
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    20 minute lightning talk presentation given by Aliaksandr Yakutovich, from École Polyechnique Fédérale de Lausanne, at the Better Science through Better Data 2018 event. The video recording and scribe are included.

  13. c

    Machine learning on multiple topological materials datasets

    • materialscloud-archive-failover.cineca.it
    • archive.materialscloud.org
    application/gzip, bin +1
    Updated Feb 26, 2025
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    Yuqing He; Pierre-Paul De Breuck; Hongming Weng; Matteo Giantomassi; Gian-Marco Rignanese; Yuqing He; Pierre-Paul De Breuck; Hongming Weng; Matteo Giantomassi; Gian-Marco Rignanese (2025). Machine learning on multiple topological materials datasets [Dataset]. http://doi.org/10.24435/materialscloud:zk-gc
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    bin, application/gzip, text/markdownAvailable download formats
    Dataset updated
    Feb 26, 2025
    Dataset provided by
    Materials Cloud
    Authors
    Yuqing He; Pierre-Paul De Breuck; Hongming Weng; Matteo Giantomassi; Gian-Marco Rignanese; Yuqing He; Pierre-Paul De Breuck; Hongming Weng; Matteo Giantomassi; Gian-Marco Rignanese
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are developed to categorize materials into five distinct topological types, with the XGBoost model achieving an impressive 85.2% classification accuracy. By conducting generalization tests on different sub-datasets, differences are identified between the original datasets in terms of topological types, chemical elements, unknown magnetic compounds, and feature space coverage. Their impact on model performance is analyzed. Turning to the simpler binary classification between trivial insulators and nontrivial topological materials, three different approaches are also tested. Key characteristics influencing material topology are identified, with the maximum packing efficiency and the fraction of p valence electrons being highlighted as critical features.

  14. m

    Exploring the magnetic landscape of easily-exfoliable two-dimensional...

    • archive.materialscloud.org
    • materialscloud-archive-failover.cineca.it
    text/markdown, txt +1
    Updated May 26, 2025
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    Fatemeh Haddadi; Davide Campi; Flaviano Dos Santos; Nicolas Mounet; Louis Ponet; Nicola Marzari; Marco Gibertini; Fatemeh Haddadi; Davide Campi; Flaviano Dos Santos; Nicolas Mounet; Louis Ponet; Nicola Marzari; Marco Gibertini (2025). Exploring the magnetic landscape of easily-exfoliable two-dimensional materials [Dataset]. http://doi.org/10.24435/materialscloud:m7-m9
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    txt, zip, text/markdownAvailable download formats
    Dataset updated
    May 26, 2025
    Dataset provided by
    Materials Cloud
    Authors
    Fatemeh Haddadi; Davide Campi; Flaviano Dos Santos; Nicolas Mounet; Louis Ponet; Nicola Marzari; Marco Gibertini; Fatemeh Haddadi; Davide Campi; Flaviano Dos Santos; Nicolas Mounet; Louis Ponet; Nicola Marzari; Marco Gibertini
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Magnetic materials often exhibit complex energy landscapes with multiple local minima, each corresponding to a self-consistent electronic structure solution. Finding the global minimum is challenging, and heuristic methods are not always guaranteed to succeed. We apply an automated workflow to systematically explore the energy landscape of 194 magnetic monolayers from the Materials Cloud 2D crystals database and determine their ground-state magnetic order. Our approach enables effective control and sampling of orbital occupation matrices, allowing rapid identification of local minima. We reveal a diverse set of self-consistent collinear metastable states, further enriched by Hubbard-corrected energy functionals with U parameters computed from first principles using linear response theory. We categorize the monolayers by their magnetic ordering and highlight promising candidates for applications.

  15. e

    Machine-learning accelerated identification of exfoliable two-dimensional...

    • b2find.eudat.eu
    Updated Oct 22, 2023
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    (2023). Machine-learning accelerated identification of exfoliable two-dimensional materials - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/b71ef031-5041-5d3d-883c-22bc20a6a7b0
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    Dataset updated
    Oct 22, 2023
    Description

    Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by weak binding energy and, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors. To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.

  16. W

    Data from: MATERIALS FOR IN SITU PROCESSING SYSTEMS

    • cloud.csiss.gmu.edu
    pdf
    Updated Aug 8, 2019
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    Energy Data Exchange (2019). MATERIALS FOR IN SITU PROCESSING SYSTEMS [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/materials-for-in-situ-processing-systems0
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    pdf(617591)Available download formats
    Dataset updated
    Aug 8, 2019
    Dataset provided by
    Energy Data Exchange
    Description

    The purpose of this work is to examine environmental effects of materials which are intended for use in in situ processing systems.

  17. o

    Cloud-SPAN Metagenomics Course: Overview

    • explore.openaire.eu
    Updated Oct 1, 2022
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    Sarah Forrester; Annabel Cansdale (2022). Cloud-SPAN Metagenomics Course: Overview [Dataset]. http://doi.org/10.5281/zenodo.7505873
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    Dataset updated
    Oct 1, 2022
    Authors
    Sarah Forrester; Annabel Cansdale
    Description

    Final release January 2023. DOIs and README updated.

  18. m

    Data from: Teaching oxidation states to neural networks

    • archive.materialscloud.org
    • materialscloud-archive-failover.cineca.it
    text/markdown, txt +1
    Updated Nov 29, 2024
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    Cristiano Malica; Nicola Marzari; Cristiano Malica; Nicola Marzari (2024). Teaching oxidation states to neural networks [Dataset]. http://doi.org/10.24435/materialscloud:w7-k1
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    txt, text/markdown, zipAvailable download formats
    Dataset updated
    Nov 29, 2024
    Dataset provided by
    Materials Cloud
    Authors
    Cristiano Malica; Nicola Marzari; Cristiano Malica; Nicola Marzari
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The accurate description of redox reactions remains a challenge for first-principles calculations, but it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with strongly localized d or f electrons. Here, we first show that DFT+U+V molecular dynamics is capable to follow the adiabatic evolution of oxidation states over time, using representative Li-ion cathode materials. In turn, this allows to develop redox-aware machine-learned potentials. We show that considering atoms with different oxidation states (as accurately predicted by DFT+U+V) as distinct species in the training leads to potentials that are able to identify the correct ground state and pattern of oxidation states for redox elements present. This is achieved, e.g., trough a combinatorial search for the lowest energy configuration. This brings the advantages of machine-learned potential to key technological applications (e.g., rechargeable batteries), which require an accurate description of the evolution of redox states.

  19. o

    Cloud-SPAN Metagenomics Course Session 2: Polishing

    • explore.openaire.eu
    Updated Oct 31, 2022
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    Annabel Cansdale; Sarah Forrester (2022). Cloud-SPAN Metagenomics Course Session 2: Polishing [Dataset]. http://doi.org/10.5281/zenodo.7506738
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    Dataset updated
    Oct 31, 2022
    Authors
    Annabel Cansdale; Sarah Forrester
    Description

    Metagenomics Polishing lesson

  20. m

    Data from: High-throughput computational screening for solid-state Li-ion...

    • archive.materialscloud.org
    • materialscloud-archive-failover.cineca.it
    application/gzip, bin +2
    Updated Apr 26, 2024
    + more versions
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    Leonid Kahle; Aris Marcolongo; Nicola Marzari; Leonid Kahle; Aris Marcolongo; Nicola Marzari (2024). High-throughput computational screening for solid-state Li-ion conductors [Dataset]. http://doi.org/10.24435/materialscloud:vg-ya
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    bin, text/markdown, txt, application/gzipAvailable download formats
    Dataset updated
    Apr 26, 2024
    Dataset provided by
    Materials Cloud
    Authors
    Leonid Kahle; Aris Marcolongo; Nicola Marzari; Leonid Kahle; Aris Marcolongo; Nicola Marzari
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    We present a computational screening of experimental structural repositories for fast Li-ion conductors, with the goal of finding new candidate materials for application as solid-state electrolytes in next-generation batteries. We start from ~1400 unique Li-containing materials, of which ~900 are insulators at the level of density-functional theory. For those, we calculate the diffusion coefficient in a highly automated fashion, using extensive molecular dynamics simulations on a potential energy surface (the recently published pinball model) fitted on first-principles forces. The ~130 most promising candidates are studied with full first-principles molecular dynamics, first at high temperature and then more extensively for the 78 most promising candidates. The results of the first-principles simulations of the candidate solid-state electrolytes found are discussed in detail.

    Update April 2024: Files are added that facilitate the Materials Cloud Archive OPTIMADE service to serve the structural data of this Archive entry via an OPTIMADE API. The molecular dynamics trajectories are served as individual structures per time-step.

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(2023). The Materials Cloud 2D database (MC2D) - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/eacb138d-4f26-5971-a5ad-82a15c5abb36

The Materials Cloud 2D database (MC2D) - Dataset - B2FIND

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Dataset updated
Oct 23, 2023
Description

Two-dimensional (2D) materials are among the most promising candidates for beyond silicon electronic and optoelectronic applications. Recently, their recognized importance, sparked a race to discover and characterize new 2D materials. Within few years the number of experimentally exfoliated or synthesized 2D materials went from a couple of dozens to few hundreds while the number theoretically predicted compounds reached a few thousands. In 2018 we first contributed to this effort with the identification of 1825 compounds that are either easily (1036) or potentially (789) exfoliable from experimentally known 3D compounds. In the present work we report on the new materials recently added to the 2D-portfolio thanks to the extension of the screening to an additional experimental database (MPDS) as well as the most up-to-date versions of the two databases (ICSD and COD) used in our previous work. This expansion led to the discovery of an additional 1252 unique monolayers bringing the total to 3077 compounds and, notably, almost doubling the number of easily exfoliable materials (2004). Moreover, we optimized the structural properties of all the materials (regardless of their binding energy or number of atoms in the unit cell) as isolated mono-layer and explored their electronic band structure. This archive entry contains the database of 2D materials in particular it contains the structural parameters for all the 3077 structures of the global Material Cloud 2D database as extracted from their bulk 3D parent, 2710 optimized 2D structures and 2345 electronic band structure together with the provenance of all data and calculations as stored by AiiDA.

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