Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC  

作　　者：Yu Xie Jonathan Vandermause Senja Ramakers Nakib H.Protik Anders Johansson Boris Kozinsky 

机构地区：[1]John A.Paulson School of Engineering and Applied Sciences,Harvard University,Cambridge,MA,USA [2]Department of Physics,Harvard University,Cambridge,MA,USA [3]Corporate Sector Research and Advance Engineering,Robert Bosch GmbH,Renningen,USA [4]Interdisciplinary Centre for Advanced Materials Simulation,Ruhr-Universität Bochum,Bochum,Germany [5]Robert Bosch LLC,Research and Technology Center,Watertown,MA,USA

出　　处：《npj Computational Materials》2023年第1期2000-2007,共8页计算材料学（英文）

基　　金：YX was supported from the US Department of Energy(DOE),Office of Science,Office of Basic Energy Sciences(BES)under Award No.DE-SC0020128;JV was supported by the National Science Foundation award number 2003725;AJ was supported by the Aker scholarship.

摘　　要：Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.Active learning methods have been recently developed to train force fields efficiently and automatically.Among them,Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions.In this work,we present a general Bayesian active learning workflow,where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors.To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation,we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude.We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide(SiC)polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured.The resulting model exhibits close agreement with both ab initio calculations and experimental measurements,and outperforms existing empirical models on vibrational and thermal properties.The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.

关 键 词：COMPUTER THERMAL utilize 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]