Deep Potentials for Materials Science
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866913285169741824 |
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| author | Wen, Tongqi Zhang, Linfeng Wang, Han E, Weinan Srolovitz, David J. |
| author_facet | Wen, Tongqi Zhang, Linfeng Wang, Han E, Weinan Srolovitz, David J. |
| contents | To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_00393 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Deep Potentials for Materials Science Wen, Tongqi Zhang, Linfeng Wang, Han E, Weinan Srolovitz, David J. Materials Science Computational Physics To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials. |
| title | Deep Potentials for Materials Science |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2203.00393 |