Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
作者机构：John A.Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeMAUSA Robert Bosch LLCResearch and Technology CenterCambridgeMAUSA
出 版 物：《npj Computational Materials》 (计算材料学（英文）)
年 卷 期：2021年第7卷第1期
页 面：361-370页
核心收录：
学科分类：07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 070102[理学-计算数学] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：Y.X.is supported by the US Department of Energy(DOE)Office of Basic Energy Sciences under Award No.DE-SC0020128 L.S.is supported by the Integrated Mesoscale Architectures for Sustainable Catalysis(IMASC),an Energy Frontier Research Center funded by the US Department of Energy(DOE)Office of Basic Energy Sciences under Award No.DE-SC0012573 A.C.is supported by the Harvard Quantum Initiative J.V.is supported by Robert Bosch LLC and the National Science Foundation(NSF),Office of Advanced Cyberinfrastructure,Award No.2003725
主 题：transformation dimensional fields
摘 要：We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional *** allows for automated active learning of models combining near-quantum accuracy,built-in uncertainty,and constant cost of evaluation that is comparable to classical analytical models,capable of simulating millions of *** this approach,we perform large-scale molecular dynamics simulations of the stability of the stanene *** discover an unusual phase transformation mechanism of 2D stanene,where ripples lead to nucleation of bilayer defects,densification into a disordered multilayer structure,followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low *** presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.
维普期刊数据库