Theoretical and Computational Chemistry

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.

Version notes

Initial version of manuscript


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