Theoretical and Computational Chemistry

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

Abstract

This work demonstrates the use of open literature data to force field paramterization via a novel approach applying Bayesian optimization. We have selected Dissipative Particle Dynamics (DPD) as the simulation method in this proof-of-concept work.

Version notes

Initial version of manuscript

Content

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