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

17 July 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Bayesian optimization technique
Dissipative Particle Dynamics Simulations
logP – Partition coefficient
force field parameterization


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