A general-purpose machine learning framework for predicting properties of inorganic materials

作     者：Logan Ward Ankit Agrawal Alok Choudhary Christopher Wolverton 

作者机构：Department of Materials Science and EngineeringNorthwestern UniversityEvanstonILUSA Department of Electrical Engineering and Computer ScienceNorthwestern UniversityEvanstonILUSA 

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

年 卷 期：2016年第2卷第1期

页      面：70-76页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：supported in part by the following grants:DARPA SIMPLEX award N66001-15-C-4036 NSF awards IIS-1343639 CCF-1409601 DOE award DESC0007456 AFOSR award FA9550-12-1-0458 supported by the Department of Defense(DoD)through the National Defense Science&Engineering Graduate Fellowship(NDSEG)Program 

主　　题：method properties inorganic 

摘      要：A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials *** prior examples have demonstrated successful models for some applications,many more applications exist where machine learning can make a strong *** enable faster development of machine-learning-based models for such applications,we have created a framework capable of being applied to a broad range of materials *** method works by using a chemically diverse list of attributes,which we demonstrate are suitable for describing a wide variety of properties,and a novel method for partitioning the data set into groups of similar materials to boost the predictive *** this manuscript,we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials,such as band gap energy and glass-forming ability.