Automated crystal structure analysis based on blackbox optimisation  

作　　者：Yoshihiko Ozaki Yuta Suzuki Takafumi Hawai Kotaro Saito Masaki Onishi Kanta Ono 

机构地区：[1]Artificial Intelligence Research Center(AIRC),National Institute of Advanced Industrial Science and Technology(AIST),Tokyo,Japan [2]GREE,Inc.,Tokyo,Japan [3]Institute of Materials Structure Science(IMSS),High Energy Accelerator Research Organization(KEK),Ibaraki,Japan [4]SOKENDAI(The Graduate University for Advanced Studies),Ibaraki,Japan [5]Paul Scherrer Institute,Villigen,Switzerland [6]Medley,Inc,Tokyo,Japan

出　　处：《npj Computational Materials》2020年第1期1022-1028,共7页计算材料学（英文）

基　　金：This work is partly supported by JST-Mirai Program,Grant Number JPMJMI19G1;K.O.and T.H.are partly supported by the Elements Strategy Initiative Center for Magnetic Materials(ESICMM),Grant Number 12016013,through the Ministry of Education,Culture,Sports,Science and Technology(MEXT);Y.S.is supported by the Japan Science and Technology Agency(JST),ACT-I,grant number JPMJPR18UE;K.S.has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No.701647.

摘　　要：In the present study,we show that time-consuming manual tuning of parameters in the Rietveld method,one of the most frequently used crystal structure analysis methods in materials science,can be automated by considering the entire trial-and-error process as a blackbox optimisation problem.The automation is successfully achieved using Bayesian optimisation,which outperforms both a human expert and an expert-system type automation despite the absence of expertise.This approach stabilises the analysis quality by eliminating human-origin variance and bias,and can be applied to various analysis methods in other areas which also suffer from similar tiresome and unsystematic manual tuning of extrinsic parameters and human-origin variance and bias.

关 键 词：ANALYSIS BLACK STRUCTURE 

分 类 号：TN32[电子电信—物理电子学]