A machine learning approach to model solute grain boundary segregation

作     者：Liam Huber Raheleh Hadian Blazej Grabowski Jörg Neugebauer 

作者机构：Max-Planck-Institut für Eisenforschung GmbHD-40237 DüsseldorfGermany 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2018年第4卷第1期

页      面：130-137页

核心收录：

学科分类：08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：This project has received funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(Grant Agreement No.639211) 

主　　题：grain solute boundary 

摘      要：Even minute amounts of one solute atom per one million bulk atoms may give rise to qualitative changes in the mechanical response and fracture resistance of modern structural *** changes are commonly related to enrichment by several orders of magnitude of the solutes at structural defects in the host *** underlying concept—segregation—is thus fundamental in materials *** include it in modern strategies of materials design,accurate and realistic computational modelling tools are ***,the enormous number of defect configurations as well as sites solutes can occupy requires models which rely on severe *** the present study we combine a high-throughput study containing more than 1 million data points with machine learning to derive a computationally highly efficient framework which opens the opportunity to model this important mechanism on a routine basis.