Artificial Intelligence Enhanced Molecular Simulations

Molecular simulations, which simulate the motions of particles according to fundamental laws of physics, have been applied to a wide range of fields from physics and materials science to biochemistry and drug discovery. Developed for computationally intensive applications, most molecular simulation...

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Bibliographic Details
Published in:Journal of chemical theory and computation 2023-07, Vol.19 (14), p.4338-4350
Main Authors: Zhang, Jun, Chen, Dechin, Xia, Yijie, Huang, Yu-Peng, Lin, Xiaohan, Han, Xu, Ni, Ningxi, Wang, Zidong, Yu, Fan, Yang, Lijiang, Yang, Yi Isaac, Gao, Yi Qin
Format: Article
Language:English
Subjects:
Algorithms
Applications programs
Artificial intelligence
Code reuse
Computer simulation
Materials science
Neural networks
Programming languages
Simulation
Software reuse
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Summary:Molecular simulations, which simulate the motions of particles according to fundamental laws of physics, have been applied to a wide range of fields from physics and materials science to biochemistry and drug discovery. Developed for computationally intensive applications, most molecular simulation software involves significant use of hard-coded derivatives and code reuse across various programming languages. In this Review, we first align the relationship between molecular simulations and artificial intelligence (AI) and reveal the coherence between the two. We then discuss how the AI platform can create new possibilities and deliver new solutions to molecular simulations, from the perspective of algorithms, programming paradigms, and even hardware. Rather than focusing solely on increasingly complex neural network models, we introduce various concepts and techniques brought about by modern AI and explore how they can be transacted to molecular simulations. To this end, we summarized several representative applications of molecular simulations enhanced by AI, including from differentiable programming and high-throughput simulations. Finally, we look ahead to promising directions that may help address existing issues in the current framework of AI-enhanced molecular simulations.
ISSN:1549-9618
1549-9626
DOI:10.1021/acs.jctc.3c00214