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 18 NATURE COMPUTATIONAL SCIENCE, 4 (1), 2024, DOI: 10.1038/s43588-023-00581-5. Abstract | BibTeX | Endnote @article{WOS:001133735500001,
title = {Constructing custom thermodynamics using deep learning},
author = {Xiaoli Chen and Beatrice W Soh and Zi-En Ooi and Eleonore Vissol-Gaudin and Haijun Yu and Kostya S Novoselov and Kedar Hippalgaonkar and Qianxiao Li},
doi = {10.1038/s43588-023-00581-5},
times_cited = {18},
year = {2024},
date = {2024-01-01},
journal = {NATURE COMPUTATIONAL SCIENCE},
volume = {4},
number = {1},
publisher = {SPRINGERNATURE},
address = {CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND},
abstract = {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.
The authors develop a general method that combines machine learning and
physics to construct macroscopic dynamics directly from microscopic
observations, leading to an intuitive understanding of polymer
stretching in elongational flow.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
The authors develop a general method that combines machine learning and
physics to construct macroscopic dynamics directly from microscopic
observations, leading to an intuitive understanding of polymer
stretching in elongational flow. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AFXiaoli Chen
Beatrice W Soh
Zi-En Ooi
Eleonore Vissol-Gaudin
Haijun Yu
Kostya S Novoselov
Kedar Hippalgaonkar
Qianxiao Li
- TIConstructing custom thermodynamics using deep learning
- SONATURE COMPUTATIONAL SCIENCE
- DTArticle
- ABOne 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.
The authors develop a general method that combines machine learning and
physics to construct macroscopic dynamics directly from microscopic
observations, leading to an intuitive understanding of polymer
stretching in elongational flow. - Z918
- PUSPRINGERNATURE
- PACAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
- VL4
- DI10.1038/s43588-023-00581-5
- UTWOS:001133735500001
- ER
- EF
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