Multi-fidelity Gaussian process based empirical potential development for Si:H nanowires  

作　　者：Moonseop Kim Huayi Yin Guang Lin 

机构地区：[1]School of Mechanical Engineering,Purdue University,West Lafayette,IN 47906-2045,USA [2]School of Computer and Information Engineering,Xiamen University of Technology,Xiamen 361024,China [3]Department of Mathematics,Purdue University,West Lafayette,IN 47906-2045,USA

出　　处：《Theoretical & Applied Mechanics Letters》2020年第3期195-201,共7页力学快报（英文版）

基　　金：We gratefully acknowledge the support from the National Science Foundation of USA(Grants DMS-1555072 and DMS-1736364).

摘　　要：In material modeling,the calculation speed using the empirical potentials is fast compared to the first principle calculations,but the results are not as accurate as of the first principle calculations.First principle calculations are accurate but slow and very expensive to calculate.In this work,first,the H-H binding energy and H2-H2 interaction energy are calculated using the first principle calculations which can be applied to the Tersoff empirical potential.Second,the H-H parameters are estimated.After fitting H-H parameters,the mechanical properties are obtained.Finally,to integrate both the low-fidelity empirical potential data and the data from the high-fidelity firstprinciple calculations,the multi-fidelity Gaussian process regression is employed to predict the HH binding energy and the H2-H2 interaction energy.Numerical results demonstrate the accuracy of the developed empirical potentials.

关 键 词：Multi-fidelity Gaussian process regression Inter-atomic potential and forces ELASTICITY 

分 类 号：O613.72[理学—无机化学] TB383.1[理学—化学]