Deep Coarse-grained Potentials via Relative Entropy Minimization

作     者：Thaler, Stephan Stupp, Maximilian Zavadlav, Julija 

作者机构：Multiscale Modeling of Fluid Materials Department of Engineering Physics and Computation TUM School of Engineering and Design Technical University of Munich Germany Munich Data Science Institute Munich Institute for Integrated Materials Energy and Process Engineering Technical University of Munich Germany 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Coarse grained modeling 

摘      要：Neural network (NN) potentials are a natural choice for coarse-grained (CG) models. Their many-body capacity allows highly accurate approximations of the potential of mean force, promising CG simulations at unprecedented accuracy. CG NN potentials trained bottom-up via force matching (FM), however, suffer from finite data effects: They rely on prior potentials for physically sound predictions outside the training data domain and the corresponding free energy surface is sensitive to errors in transition regions. The standard alternative to FM for classical potentials is relative entropy (RE) minimization, which has not yet been applied to NN potentials. In this work, we demonstrate for benchmark problems of liquid water and alanine dipeptide that RE training is more data efficient due to accessing the CG distribution during training, resulting in improved free energy surfaces and reduced sensitivity to prior potentials. In addition, RE learns to correct time integration errors, allowing larger time steps in CG molecular dynamics simulation while maintaining accuracy. Thus, our findings support the use of training objectives beyond FM as a promising direction for improving CG NN potential accuracy and reliability. © 2022, CC BY.