Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials  

作　　者：Carla Verdi Ferenc Karsai Peitao Liu Ryosuke Jinnouchi Georg Kresse 

机构地区：[1]University of Vienna,Faculty of Physics,Computational Materials Physics,Kolingasse 14-16,1090 Vienna,Austria [2]VASP Software GmbH,Sensengasse 8,1090 Vienna,Austria [3]Toyota Central R&D Labs.,Inc.,Aichi 480-1192,Japan

出　　处：《npj Computational Materials》2021年第1期1426-1434,共9页计算材料学（英文）

基　　金：This work was supported by the Austan Sckence FundFWF(SFB TACO)PL gratefulty acimowled ges the support of the Advanced Matetals Smulaton Enginearing Tool(AMSET)project;sponsored by the US Navai Nuckear Laboratory(NNL)and directed by Matarlals Design,Inc.

摘　　要：Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with firstprinciples accuracy.It is not yet clear,however,how accurately they describe anharmonic properties,which are crucial for predicting the lattice thermal conductivity and phase transitions in solids and,thus,shape their technological applications.Here we employ a recently developed on-the-fly learning technique based on molecular dynamics and Bayesian inference in order to generate an interatomic potential capable to describe the thermodynamic properties of zirconia,an important transition metal oxide.This machine-learned potential accurately captures the temperature-induced phase transitions below the melting point.We further showcase the predictive power of the potential by calculating the heat transport on the basis of Green–Kubo theory,which allows to account for anharmonic effects to all orders.This study indicates that machine-learned potentials trained on the fly offer a routine solution for accurate and efficient simulations of the thermodynamic properties of a vast class of anharmonic materials.

关 键 词：MATERIALS TRANSITIONS HARMONIC 

分 类 号：O61[理学—无机化学]