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Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields
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DOI：10.1021/acs.jcim.9b00646.png)
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Abstract
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We present a machine learning approach to automated force field development in dissipative particle dynamics (DPD). The approach employs Bayesian optimization to parametrize a DPD force field against experimentally determined partition coefficients. The optimization process covers a discrete space of over 40 000 000 points, where each point represents the set of potentials that jointly forms a force field. We find that Bayesian optimization is capable of reaching a force field of comparable performance to the current state-of-the-art within 40 iterations. The best iteration during the optimization achieves an R-2 of 0.78 and an RMSE of 0.63 log units on the training set of data, these metrics are maintained when a validation set is included, giving R-2 of 0.8 and an RMSE of 0.65 log units. This work hence provides a proof-of-concept, expounding the utility of coupling automated and efficient global optimization with a top down data driven approach to force field parametrization. Compared to commonly employed alternative methods, Bayesian optimization offers global parameter searching and a low time to solution.
Keywords:
ELECTRON CORRELATION ENERGIES
PARTITION-COEFFICIENTS
ORGANIC-COMPOUNDS
N-HEXANE/WATER
DYNAMICS
MODELS
SIMULATION
PREDICTION
CHARMM
EXPLOITATION
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