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Machine Learning Directed Optimization of Classical Molecular Modeling Force Fields
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AI
DOI：10.1021/acs.jcim.1c00448.png)
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Abstract
En 中文
Accurate force fields are necessary for predictive molecular simulations. However, developing force fields that accurately reproduce experimental properties is challenging. Here, we present a machine learning directed, multiobjective optimization workflow for force field parametrization that evaluates millions of prospective force field parameter sets while requiring only a small fraction of them to be tested with molecular simulations. We demonstrate the generality of the approach and identify multiple low-error parameter sets for two distinct test cases: simulations of hydrofluorocarbon (HFC) vapor-liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal phase. We discuss the challenges and implications of our force field optimization workflow.
Keywords:
UNCERTAINTY QUANTIFICATION
PARAMETERS
DYNAMICS
SIMULATION
GROMOS
AMBER
OPLS
PARAMETRIZATION
MECHANICS
FRAMEWORK
Journal
IF:
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Citations:
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