Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields.

Abstract

We present a machine learning approach to automated force field development in Dissipative Particle Dynamics (DPD). The approach employs Bayesian optimization to parameterize 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 form a force field. We find that Bayesian optimization is capable of reaching a force field of comparable performance to the the current state-of-the-art within 40 iterations. The best iteration during the optimization achieves an R2 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 R2 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 parameterization. Compared to commonly employed alternative methods, Bayesian optimization offers global parameter searching and a low time to solution.