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

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.