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Creators/Authors contains: "Maginn, E.J."

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  1. ; ; ; (, Computing and computers)
    Yamashita, Y.; Kano, M. (Ed.)
    In this work surrogate assisted optimization is utilized to calibrate predictive molecular models, called force fields, used in molecular simulations to reproduce the liquid density of a hydrofluorocarbon refrigerant molecule. A previous calibration workflow which relied on Gaussian process regression models and large Latin hypercube samples to screen force field parameter space is extended to include Bayesian optimization methods to efficiently guide the search for force field parameters. In comparison to the previous work, the Bayesian-based calibration workflow finds a parameter set which results in a lower objective function value than the original workflow after evaluating approximately 50% fewer parameter sets. It is envisioned that this updated workflow will facilitate rapid force field optimization enabling screening of vast molecular design space. 
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