Machine‐learning‐based interatomic potentials for advanced manufacturing
Abstract This paper summarizes the progress of machine‐learning‐based interatomic potentials and their applications in advanced manufacturing. Interatomic potential is essential for classical molecular dynamics. The advancements made in machine learning (ML) have enabled the development of fast inte...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-12-01
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| Series: | International Journal of Mechanical System Dynamics |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/msd2.12021 |
| _version_ | 1818759774517329920 |
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| author | Wei Yu Chaoyue Ji Xuhao Wan Zhaofu Zhang John Robertson Sheng Liu Yuzheng Guo |
| author_facet | Wei Yu Chaoyue Ji Xuhao Wan Zhaofu Zhang John Robertson Sheng Liu Yuzheng Guo |
| author_sort | Wei Yu |
| collection | DOAJ |
| description | Abstract This paper summarizes the progress of machine‐learning‐based interatomic potentials and their applications in advanced manufacturing. Interatomic potential is essential for classical molecular dynamics. The advancements made in machine learning (ML) have enabled the development of fast interatomic potential with ab initio accuracy. The accelerated atomic simulation can greatly transform the design principle of manufacturing technology. The most widely used supervised and unsupervised ML methods are summarized and compared. Then, the emerging interatomic models based on ML are discussed: Gaussian approximation potential, spectral neighbor analysis potential, deep potential molecular dynamics, SCHNET, hierarchically interacting particle neural network, and fast learning of atomistic rare events. |
| first_indexed | 2024-12-18T06:48:04Z |
| format | Article |
| id | doaj.art-2d03664a4e8c436dbf38225c36aae5a6 |
| institution | Directory Open Access Journal |
| issn | 2767-1402 |
| language | English |
| last_indexed | 2024-12-18T06:48:04Z |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Mechanical System Dynamics |
| spelling | doaj.art-2d03664a4e8c436dbf38225c36aae5a62022-12-21T21:17:25ZengWileyInternational Journal of Mechanical System Dynamics2767-14022021-12-011215917210.1002/msd2.12021Machine‐learning‐based interatomic potentials for advanced manufacturingWei Yu0Chaoyue Ji1Xuhao Wan2Zhaofu Zhang3John Robertson4Sheng Liu5Yuzheng Guo6School of Electrical Engineering and Automation Wuhan University Wuhan ChinaInstitute of Technological Sciences Wuhan University Wuhan ChinaSchool of Electrical Engineering and Automation Wuhan University Wuhan ChinaEngineering Department Cambridge University Cambridge UKSchool of Electrical Engineering and Automation Wuhan University Wuhan ChinaInstitute of Technological Sciences Wuhan University Wuhan ChinaSchool of Electrical Engineering and Automation Wuhan University Wuhan ChinaAbstract This paper summarizes the progress of machine‐learning‐based interatomic potentials and their applications in advanced manufacturing. Interatomic potential is essential for classical molecular dynamics. The advancements made in machine learning (ML) have enabled the development of fast interatomic potential with ab initio accuracy. The accelerated atomic simulation can greatly transform the design principle of manufacturing technology. The most widely used supervised and unsupervised ML methods are summarized and compared. Then, the emerging interatomic models based on ML are discussed: Gaussian approximation potential, spectral neighbor analysis potential, deep potential molecular dynamics, SCHNET, hierarchically interacting particle neural network, and fast learning of atomistic rare events.https://doi.org/10.1002/msd2.12021advanced manufacturinginteratomic potentialmachine learningmolecular dynamics |
| spellingShingle | Wei Yu Chaoyue Ji Xuhao Wan Zhaofu Zhang John Robertson Sheng Liu Yuzheng Guo Machine‐learning‐based interatomic potentials for advanced manufacturing International Journal of Mechanical System Dynamics advanced manufacturing interatomic potential machine learning molecular dynamics |
| title | Machine‐learning‐based interatomic potentials for advanced manufacturing |
| title_full | Machine‐learning‐based interatomic potentials for advanced manufacturing |
| title_fullStr | Machine‐learning‐based interatomic potentials for advanced manufacturing |
| title_full_unstemmed | Machine‐learning‐based interatomic potentials for advanced manufacturing |
| title_short | Machine‐learning‐based interatomic potentials for advanced manufacturing |
| title_sort | machine learning based interatomic potentials for advanced manufacturing |
| topic | advanced manufacturing interatomic potential machine learning molecular dynamics |
| url | https://doi.org/10.1002/msd2.12021 |
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