期刊
MOLECULAR PHYSICS
卷 118, 期 5, 页码 -出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00268976.2020.1737742
关键词
machine learning; molecular simulation; deep learning; enhanced sampling; collective variables
资金
- MICCoM (Midwest Center for Computational Materials), as part of the Computational Materials Science Program - U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division
- National Science Foundation [CHE-1841805]
- National Science Foundation Molecular Software Sciences Institute (MolSSI) Software Fellows program [ACI-1547580, 205, 206]
Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning - especially deep learning - to molecular simulation. These techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.
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