preprint · ChemRxiv · 2019

Deep Learning for Variational Multi-Scale Molecular Modeling

Jun Zhang, Yao-Kun Lei, Yi Yang, Yi Qin Gao

Cited by 3Open access

Abstract

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method defines a variational and adaptive training objective which allows end-to-end training of parametric molecular models using deep neural networks. Through multiple experiments, we show that our method is efficient and flexible, and performs well on challenging chemical and bio-molecular systems.

Cite this paper

APA 7

Zhang, J., Lei, Y.-K., Yang, Y., & Gao, Y. Q. (2019). Deep Learning for Variational Multi-Scale Molecular Modeling. ChemRxiv. https://doi.org/10.26434/chemrxiv.9640814

MLA 9

Zhang, Jun, et al. “Deep Learning for Variational Multi-Scale Molecular Modeling.” ChemRxiv, 2019, https://doi.org/10.26434/chemrxiv.9640814.

Chicago

Zhang, Jun, Yao-Kun Lei, Yi Yang, and Yi Qin Gao. 2019. “Deep Learning for Variational Multi-Scale Molecular Modeling.” ChemRxiv. https://doi.org/10.26434/chemrxiv.9640814.

Harvard

Zhang, J. et al. (2019) ‘Deep Learning for Variational Multi-Scale Molecular Modeling’, ChemRxiv. Available at: https://doi.org/10.26434/chemrxiv.9640814.

BibTeX

@misc{zhang2019deep,
  title = {{Deep Learning for Variational Multi-Scale Molecular Modeling}},
  author = {Zhang, Jun and Lei, Yao-Kun and Yang, Yi and Gao, Yi Qin},
  journal = {ChemRxiv},
  year = {2019},
  doi = {10.26434/chemrxiv.9640814}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.