Coarse-graining auto-encoders for molecular dynamics  

作　　者：Wujie Wang Rafael Gómez-Bombarelli 

机构地区：[1]Department of Materials Science and Engineering,Massachusets Institute of Technology,77 Massachusetts Avenue,Cambridge,MA 02319,USA

出　　处：《npj Computational Materials》2019年第1期48-56,共9页计算材料学（英文）

基　　金：W.W.thanks Toyota Research Institutefor financial support

摘　　要：Molecular dynamics simulations provide theoretical insight into the microscopic behavior of condensed-phase materials and,as a predictive tool,enable computational design of new compounds.However,because of the large spatial and temporal scales of thermodynamic and kinetic phenomena in materials,atomistic simulations are often computationally infeasible.Coarse-graining methods allow larger systems to be simulated by reducing their dimensionality,propagating longer timesteps,and averaging out fast motions.Coarse-graining involves two coupled learning problems:defining the mapping from an all-atom representation to a reduced representation,and parameterizing a Hamiltonian over coarse-grained coordinates.We propose a generative modeling framework based on variational auto-encoders to unify the tasks of learning discrete coarse-grained variables,decoding back to atomistic detail,and parameterizing coarse-grained force fields.The framework is tested on a number of model systems including single molecules and bulk-phase periodic simulations.

关 键 词：dynamics REPRESENTATION STEPS 

分 类 号：O17[理学—数学]