Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations
Active learning (AL) requires massive time for comprehensive sampling of complex potential energy surfaces to achieve desirable accuracy and stability of machine learning (ML) potentials. Here, we develop an active delta-learning (ADL) protocol for speeding up AL and building delta-learning models y...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adeb46 |
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| author | Yaohuang Huang Yi-Fan Hou Pavlo O Dral |
| author_facet | Yaohuang Huang Yi-Fan Hou Pavlo O Dral |
| author_sort | Yaohuang Huang |
| collection | DOAJ |
| description | Active learning (AL) requires massive time for comprehensive sampling of complex potential energy surfaces to achieve desirable accuracy and stability of machine learning (ML) potentials. Here, we develop an active delta-learning (ADL) protocol for speeding up AL and building delta-learning models yielding stable simulations. ADL converges after a few iterations and needs tenfold fewer sampled points than without delta-learning while leading to models of similar accuracy, as we show on the test simulations of Diels–Alder reactions. The test reactions include one small (ethene + 1,3-butadiene) and one relatively big (C _60 + 2,3-dimethyl-1,3-butadiene) system, treated with a target density functional theory level (U)B3LYP(-D4)/6-31G* and a baseline semi-empirical quantum mechanical method, GFN2-xTB. The crucial advantage of the models built with the delta-learning protocol is their remarkable simulation stability: even models from the initial ADL iterations yield reasonable results. In contrast, the pure ML potentials built without delta-learning often lead to the collapse in simulations, i.e. to unphysical structures. |
| format | Article |
| id | doaj-art-7ca137dee62746ab98402fe1c83ccbd8 |
| institution | OA Journals |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-7ca137dee62746ab98402fe1c83ccbd82025-08-20T02:37:18ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016303500410.1088/2632-2153/adeb46Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulationsYaohuang Huang0https://orcid.org/0000-0001-6942-3499Yi-Fan Hou1https://orcid.org/0000-0001-9188-5323Pavlo O Dral2https://orcid.org/0000-0002-2975-9876State Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University , Xiamen 361005, People’s Republic of ChinaState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University , Xiamen 361005, People’s Republic of ChinaState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University , Xiamen 361005, People’s Republic of China; Institute of Physics, Faculty of Physics, Astronomy, and Informatics, Nicolaus Copernicus University in Toruń , ul. Grudziądzka 5, 87-100 Toruń, Poland; Aitomistic , Shenzhen 518000, People’s Republic of ChinaActive learning (AL) requires massive time for comprehensive sampling of complex potential energy surfaces to achieve desirable accuracy and stability of machine learning (ML) potentials. Here, we develop an active delta-learning (ADL) protocol for speeding up AL and building delta-learning models yielding stable simulations. ADL converges after a few iterations and needs tenfold fewer sampled points than without delta-learning while leading to models of similar accuracy, as we show on the test simulations of Diels–Alder reactions. The test reactions include one small (ethene + 1,3-butadiene) and one relatively big (C _60 + 2,3-dimethyl-1,3-butadiene) system, treated with a target density functional theory level (U)B3LYP(-D4)/6-31G* and a baseline semi-empirical quantum mechanical method, GFN2-xTB. The crucial advantage of the models built with the delta-learning protocol is their remarkable simulation stability: even models from the initial ADL iterations yield reasonable results. In contrast, the pure ML potentials built without delta-learning often lead to the collapse in simulations, i.e. to unphysical structures.https://doi.org/10.1088/2632-2153/adeb46machine learning interatomic potentialsdelta learningactive learningchemical reactionsmolecular dynamicstransition state |
| spellingShingle | Yaohuang Huang Yi-Fan Hou Pavlo O Dral Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations Machine Learning: Science and Technology machine learning interatomic potentials delta learning active learning chemical reactions molecular dynamics transition state |
| title | Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations |
| title_full | Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations |
| title_fullStr | Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations |
| title_full_unstemmed | Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations |
| title_short | Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations |
| title_sort | active delta learning for fast construction of interatomic potentials and stable molecular dynamics simulations |
| topic | machine learning interatomic potentials delta learning active learning chemical reactions molecular dynamics transition state |
| url | https://doi.org/10.1088/2632-2153/adeb46 |
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