Principal component analysis enables the design of deep learning potential precisely capturing LLZO phase transitions  

作　　者：Yiwei You Dexin Zhang Fulun Wu Xinrui Cao Yang Sun Zi-Zhong Zhu Shunqing Wu 

机构地区：[1]Department of Physics,OSED,Key Laboratory of Low Dimensional Condensed Matter Physics(Department of Education of Fujian Province),Xiamen University,Xiamen 361005,China [2]Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry,Xiamen University,Xiamen 361005,China

出　　处：《npj Computational Materials》2024年第1期2648-2656,共9页计算材料学(英文)

基　　金：supported by the National Natural Science Foundation of China(11874307).

摘　　要：The development of accurate and efficient interatomic potentials using machine learning has emerged as an important approach in materials simulations and discovery.However,the systematic construction of diverse,converged training sets remains challenging.

关 键 词：TRANSITIONS enable precisely 

分 类 号：O73[理学—晶体学]