Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials  

作　　者：Dongsun Yoo Jisu Jung Wonseok Jeong Seungwu Han 

机构地区：[1]Department of Materials Science and Engineering and Research Institute of Advanced Materials,Seoul National University,Seoul,Korea [2]Samsung Display Co.,Ltd.,1,Samsung-ro,Giheung-gu,Yongin-si,Gyeonggi-do,Korea

出　　处：《npj Computational Materials》2021年第1期1171-1179,共9页计算材料学（英文）

基　　金：This work was supported by Samsung Electronics(IO201214-08143-01);The computations were carried out at Korea Institute of Science and Technology Information(KISTI)National Supercomputing Center(KSC-2020-CRE-0125).

摘　　要：The universal mathematical form of machine-learning potentials(MLPs)shifts the core of development of interatomic potentials to collecting proper training data.Ideally,the training set should encompass diverse local atomic environments but conventional approaches are prone to sampling similar configurations repeatedly,mainly due to the Boltzmann statistics.As such,practitioners handpick a large pool of distinct configurations manually,stretching the development period significantly.To overcome this hurdle,methods are being proposed that automatically generate training data.Herein,we suggest a sampling method optimized for gathering diverse yet relevant configurations semi-automatically.This is achieved by applying the metadynamics with the descriptor for the local atomic environment as a collective variable.As a result,the simulation is automatically steered toward unvisited local environment space such that each atom experiences diverse chemical environments without redundancy.We apply the proposed metadynamics sampling to H:Pt(111),GeTe,and Si systems.Throughout these examples,a small number of metadynamics trajectories can provide reference structures necessary for training high-fidelity MLPs.By proposing a semiautomatic sampling method tuned for MLPs,the present work paves the way to wider applications of MLPs to many challenging applications.

关 键 词：dynamics COLLECTIVE STRETCHING 

分 类 号：O562[理学—原子与分子物理] TP181[理学—物理]