# Contrastive Learning of Coarse-Grained Force Fields

@article{Ding2022ContrastiveLO, title={Contrastive Learning of Coarse-Grained Force Fields}, author={Xinqiang Ding and Bin W. Zhang}, journal={Journal of chemical theory and computation}, year={2022} }

Coarse-grained models have proven helpful for simulating complex systems over long time scales to provide molecular insights into various processes. Methodologies for systematic parametrization of the underlying energy function or force field that describes the interactions among different components of the system are of great interest for ensuring simulation accuracy. We present a new method, potential contrasting, to enable efficient learning of force fields that can accurately reproduce the…

## One Citation

### Deep Coarse-grained Potentials via Relative Entropy Minimization

- Computer Science
- 2022

It is demonstrated for benchmark problems of liquid water and alanine dipeptide that RE training is more data due to accessing the CG distribution during training, resulting in improved free energy surfaces and reduced sensitivity to prior potentials.

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