Constructing custom thermodynamics using deep learning
One of the most exciting applications of artificial intelligence is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can as...
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sg-ntu-dr.10356-1762042024-05-17T15:50:21Z Constructing custom thermodynamics using deep learning Chen, Xiaoli Soh, Beatrice W. Ooi, Zi-En Vissol-Gaudin, Eleonore Yu, Haijun Novoselov, Kostya S. Hippalgaonkar, Kedar Li, Qianxiao School of Materials Science and Engineering Institute of Materials Research and Engineering, A*STAR Engineering Applied field Dissipative systems One of the most exciting applications of artificial intelligence is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. Our method simultaneously constructs reduced thermodynamic coordinates and interprets the dynamics on these coordinates. We demonstrate its effectiveness by studying theoretically and validating experimentally the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate. Our general methodology can be used to address a wide range of scientific and technological applications. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) National Research Foundation (NRF) Published version This research is supported by the Ministry of Education, Singapore, under its Research Centre of Excellence award to the Institute for Functional Intelligent Materials (I-FIM, project no. EDUNC-33-18- 279-V12 KSN). K.S.N. is grateful to the Royal Society (UK, grant number RSRP \R \190000 KSN) for support. Q.L. acknowledges support from the National Research Foundation, Singapore, under the NRF fellowship (project no. NRF-NRFF13-2021-0005 QL). H.Y. acknowledges support from the National Natural Science Foundation of China under Grant No. 12171467 HY and 12161141017 HY. K.H., B.W.S. and Z.-E.O. acknowledge support from the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research under Grant No. A1898b0043 KH. 2024-05-14T01:30:46Z 2024-05-14T01:30:46Z 2024 Journal Article Chen, X., Soh, B. W., Ooi, Z., Vissol-Gaudin, E., Yu, H., Novoselov, K. S., Hippalgaonkar, K. & Li, Q. (2024). Constructing custom thermodynamics using deep learning. Nature Computational Science, 4(1), 66-85. https://dx.doi.org/10.1038/s43588-023-00581-5 2662-8457 https://hdl.handle.net/10356/176204 10.1038/s43588-023-00581-5 38200379 2-s2.0-85180861600 1 4 66 85 en A1898b0043 Nature Computational Science © 2023 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ application/pdf |
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Engineering Applied field Dissipative systems Chen, Xiaoli Soh, Beatrice W. Ooi, Zi-En Vissol-Gaudin, Eleonore Yu, Haijun Novoselov, Kostya S. Hippalgaonkar, Kedar Li, Qianxiao Constructing custom thermodynamics using deep learning |
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One of the most exciting applications of artificial intelligence is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. Our method simultaneously constructs reduced thermodynamic coordinates and interprets the dynamics on these coordinates. We demonstrate its effectiveness by studying theoretically and validating experimentally the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate. Our general methodology can be used to address a wide range of scientific and technological applications. |
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School of Materials Science and Engineering |
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School of Materials Science and Engineering Chen, Xiaoli Soh, Beatrice W. Ooi, Zi-En Vissol-Gaudin, Eleonore Yu, Haijun Novoselov, Kostya S. Hippalgaonkar, Kedar Li, Qianxiao |
format |
Article |
author |
Chen, Xiaoli Soh, Beatrice W. Ooi, Zi-En Vissol-Gaudin, Eleonore Yu, Haijun Novoselov, Kostya S. Hippalgaonkar, Kedar Li, Qianxiao |
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Chen, Xiaoli |
title |
Constructing custom thermodynamics using deep learning |
title_short |
Constructing custom thermodynamics using deep learning |
title_full |
Constructing custom thermodynamics using deep learning |
title_fullStr |
Constructing custom thermodynamics using deep learning |
title_full_unstemmed |
Constructing custom thermodynamics using deep learning |
title_sort |
constructing custom thermodynamics using deep learning |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/176204 |
_version_ |
1806059879836680192 |