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

@article{McDonagh2019UtilizingML,
  title={Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields},
  author={James L. McDonagh and Ardita Shkurti and David J Bray and Richard L. Anderson and Edward O. Pyzer-Knapp},
  journal={Journal of chemical information and modeling},
  year={2019}
}
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… 

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