Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt
Uncertainty-aware machine learning models are used to automate the training of reactive force fields. The method is used here to simulate hydrogen turnover on a platinum surface with unprecedented accuracy.
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-09-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-32294-0 |
| _version_ | 1828143959026696192 |
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| author | Jonathan Vandermause Yu Xie Jin Soo Lim Cameron J. Owen Boris Kozinsky |
| author_facet | Jonathan Vandermause Yu Xie Jin Soo Lim Cameron J. Owen Boris Kozinsky |
| author_sort | Jonathan Vandermause |
| collection | DOAJ |
| description | Uncertainty-aware machine learning models are used to automate the training of reactive force fields. The method is used here to simulate hydrogen turnover on a platinum surface with unprecedented accuracy. |
| first_indexed | 2024-04-11T20:07:58Z |
| format | Article |
| id | doaj.art-4fdade2ecf7f418689a47025b578bb55 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-11T20:07:58Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-4fdade2ecf7f418689a47025b578bb552022-12-22T04:05:16ZengNature PortfolioNature Communications2041-17232022-09-0113111210.1038/s41467-022-32294-0Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/PtJonathan Vandermause0Yu Xie1Jin Soo Lim2Cameron J. Owen3Boris Kozinsky4Department of Physics, Harvard UniversityJohn A. Paulson School of Engineering and Applied Sciences, Harvard UniversityDepartment of Chemistry and Chemical Biology, Harvard UniversityDepartment of Chemistry and Chemical Biology, Harvard UniversityJohn A. Paulson School of Engineering and Applied Sciences, Harvard UniversityUncertainty-aware machine learning models are used to automate the training of reactive force fields. The method is used here to simulate hydrogen turnover on a platinum surface with unprecedented accuracy.https://doi.org/10.1038/s41467-022-32294-0 |
| spellingShingle | Jonathan Vandermause Yu Xie Jin Soo Lim Cameron J. Owen Boris Kozinsky Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt Nature Communications |
| title | Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt |
| title_full | Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt |
| title_fullStr | Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt |
| title_full_unstemmed | Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt |
| title_short | Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt |
| title_sort | active learning of reactive bayesian force fields applied to heterogeneous catalysis dynamics of h pt |
| url | https://doi.org/10.1038/s41467-022-32294-0 |
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