BIGDML—Towards accurate quantum machine learning force fields for materials
Most machine-learning force fields dismiss long-range interactions. Here the authors demonstrate the BIGDML approach for building materials’ potential energy surfaces that enables a broad range of materials simulations within accuracies better than 1 meV/atom using just 10–200 structures for trainin...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-31093-x |
| _version_ | 1818234256872177664 |
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| author | Huziel E. Sauceda Luis E. Gálvez-González Stefan Chmiela Lauro Oliver Paz-Borbón Klaus-Robert Müller Alexandre Tkatchenko |
| author_facet | Huziel E. Sauceda Luis E. Gálvez-González Stefan Chmiela Lauro Oliver Paz-Borbón Klaus-Robert Müller Alexandre Tkatchenko |
| author_sort | Huziel E. Sauceda |
| collection | DOAJ |
| description | Most machine-learning force fields dismiss long-range interactions. Here the authors demonstrate the BIGDML approach for building materials’ potential energy surfaces that enables a broad range of materials simulations within accuracies better than 1 meV/atom using just 10–200 structures for training. |
| first_indexed | 2024-12-12T11:35:11Z |
| format | Article |
| id | doaj.art-12448a9a30fc406b930ba187509c527b |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-12T11:35:11Z |
| publishDate | 2022-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-12448a9a30fc406b930ba187509c527b2022-12-22T00:25:40ZengNature PortfolioNature Communications2041-17232022-06-0113111610.1038/s41467-022-31093-xBIGDML—Towards accurate quantum machine learning force fields for materialsHuziel E. Sauceda0Luis E. Gálvez-González1Stefan Chmiela2Lauro Oliver Paz-Borbón3Klaus-Robert Müller4Alexandre Tkatchenko5Departamento de Materia Condensada, Instituto de Física, Universidad Nacional Autónoma de MéxicoPrograma de Doctorado en Ciencias (Física), División de Ciencias Exactas y Naturales, Universidad de Sonora, Blvd. Luis Encinas & RosalesMachine Learning Group, Technische Universität BerlinDepartamento de Física Química, Instituto de Física, Universidad Nacional Autónoma de MéxicoMachine Learning Group, Technische Universität BerlinDepartment of Physics and Materials Science, University of LuxembourgMost machine-learning force fields dismiss long-range interactions. Here the authors demonstrate the BIGDML approach for building materials’ potential energy surfaces that enables a broad range of materials simulations within accuracies better than 1 meV/atom using just 10–200 structures for training.https://doi.org/10.1038/s41467-022-31093-x |
| spellingShingle | Huziel E. Sauceda Luis E. Gálvez-González Stefan Chmiela Lauro Oliver Paz-Borbón Klaus-Robert Müller Alexandre Tkatchenko BIGDML—Towards accurate quantum machine learning force fields for materials Nature Communications |
| title | BIGDML—Towards accurate quantum machine learning force fields for materials |
| title_full | BIGDML—Towards accurate quantum machine learning force fields for materials |
| title_fullStr | BIGDML—Towards accurate quantum machine learning force fields for materials |
| title_full_unstemmed | BIGDML—Towards accurate quantum machine learning force fields for materials |
| title_short | BIGDML—Towards accurate quantum machine learning force fields for materials |
| title_sort | bigdml towards accurate quantum machine learning force fields for materials |
| url | https://doi.org/10.1038/s41467-022-31093-x |
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