Data

Ruthenium Nanoparticle Data Set

Commonwealth Scientific and Industrial Research Organisation
Barnard, Amanda ; Opletal, George
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/5e30b8fa67484&rft.title=Ruthenium Nanoparticle Data Set&rft.identifier=https://doi.org/10.25919/5e30b8fa67484&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=This is a set of 2500 ruthenium (Ru) nanoparticle FINAL CONFIGURATIONS, for use in data-driven studies. These structures have been optimized (fully relaxed) using molecular dynamics with an embedded atom (EAM) interatomic potential, at various temperatures and growth rates.\n\nAll files are in XYZ format, and the naming convention is defined in the accompanying csv file that lists all of the structural features and property indicators (see Supporting Attachments). \n\nSizes range from 61 atoms to 17428 atoms, with both crystalline and non-crystalline configurations and regions. Each nanoparticle has been characterised using a variety of topological features, including size, lattice structure, surface curvature and a number of order parameters. The final five columns in the accompanying csv file are target labels, providing the concentration of classes of surface structures responsible for different types of catalytic reactions, the total energy and the excess formation energy. Links to publications describing these property labels are provided in the Supporting Attachments. Other features can also be used as labels as desired.\nLineage: Simulated by Amanda Barnard for the purposes of studying the impact of polydispersivity on the properties of ruthenium nanoparticle ensembles.\n&rft.creator=Barnard, Amanda &rft.creator=Opletal, George &rft.date=2020&rft.edition=v1&rft.relation=https://pubs.rsc.org/en/content/articlelanding/2018/nr/c8nr06450d#!divAbstract&rft.relation=https://pubs.acs.org/doi/10.1021/acs.jpcc.8b08386&rft.relation=https://pubs.rsc.org/en/content/articlelanding/2017/nr/c6nr06765d#!divAbstract&rft.relation=https://pubs.rsc.org/en/Content/ArticleLanding/CY/2016/C5CY01205H#!divAbstract&rft_rights=CSIRO Data Licence https://research.csiro.au/dap/licences/csiro-data-licence/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2019.&rft_subject=material&rft_subject=molecular dynamics&rft_subject=nanoparticle&rft_subject=nanostructure&rft_subject=computational&rft_subject=simulation&rft_subject=results&rft_subject=ruthenium&rft_subject=xyz&rft_subject=configurations&rft_subject=structures&rft_subject=Theory and design of materials&rft_subject=Macromolecular and materials chemistry&rft_subject=CHEMICAL SCIENCES&rft_subject=Theoretical and computational chemistry not elsewhere classified&rft_subject=Theoretical and computational chemistry&rft_subject=Nanomaterials&rft_subject=Nanotechnology&rft_subject=ENGINEERING&rft_subject=Nanotechnology not elsewhere classified&rft_subject=Artificial intelligence not elsewhere classified&rft_subject=Artificial intelligence&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=Data mining and knowledge discovery&rft_subject=Data management and data science&rft_subject=Condensed matter modelling and density functional theory&rft_subject=Condensed matter physics&rft_subject=PHYSICAL SCIENCES&rft_subject=Condensed matter physics not elsewhere classified&rft.type=dataset&rft.language=English Access the data
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This is a set of 2500 ruthenium (Ru) nanoparticle FINAL CONFIGURATIONS, for use in data-driven studies. These structures have been optimized (fully relaxed) using molecular dynamics with an embedded atom (EAM) interatomic potential, at various temperatures and growth rates.

All files are in XYZ format, and the naming convention is defined in the accompanying csv file that lists all of the structural features and property indicators (see Supporting Attachments).

Sizes range from 61 atoms to 17428 atoms, with both crystalline and non-crystalline configurations and regions. Each nanoparticle has been characterised using a variety of topological features, including size, lattice structure, surface curvature and a number of order parameters. The final five columns in the accompanying csv file are target labels, providing the concentration of classes of surface structures responsible for different types of catalytic reactions, the total energy and the excess formation energy. Links to publications describing these property labels are provided in the Supporting Attachments. Other features can also be used as labels as desired.
Lineage: Simulated by Amanda Barnard for the purposes of studying the impact of polydispersivity on the properties of ruthenium nanoparticle ensembles.

Available: 2020-01-29

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Artificial Intelligence | Artificial Intelligence Not Elsewhere Classified | Chemical Sciences | Condensed Matter Modelling and Density Functional Theory | Condensed Matter Physics | Condensed Matter Physics Not Elsewhere Classified | Data Management and Data Science | Data Mining and Knowledge Discovery | Engineering | Information and Computing Sciences | Macromolecular and Materials Chemistry | Nanomaterials | Nanotechnology | Nanotechnology Not Elsewhere Classified | Physical Sciences | Theoretical and Computational Chemistry | Theoretical and Computational Chemistry Not Elsewhere Classified | Theory and Design of Materials | computational | configurations | material | molecular dynamics | nanoparticle | nanostructure | results | ruthenium | simulation | structures | xyz |

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