# 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…

## 27 Citations

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### Coarse-grained molecular dynamics study based on TorchMD

- Computer ScienceChinese Journal of Chemical Physics
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The workflow in this work provides another option to study the protein folding and other relative processes with the deep learning CG model and shows that the main phenomenon of protein folding with TorchMD CG model is the same as the all-atom simulations, but with a less simulating time scale.

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