Accelerating materials discovery using artificial intelligence, high performance computing and robotics

作     者：Edward O.Pyzer-Knapp Jed W.Pitera Peter W.J.Staar Seiji Takeda Teodoro Laino Daniel P.Sanders James Sexton John R.Smith Alessandro Curioni 

作者机构：IBM Research Europe-DaresburyDaresburyUK IBM Almaden Research CentreSan JoseCAUSA IBM Research Europe ZurichRüschlikonSwitzerland IBM Research TokyoTokyoJapan IBM Thomas J.Watson Research CentreYorktown HeightsNYUSA 

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

年 卷 期：2022年第8卷第1期

页      面：767-775页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080202[工学-机械电子工程] 081104[工学-模式识别与智能系统] 08[工学] 0804[工学-仪器科学与技术] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors would like to acknowledge their IBM colleagues for their continued contributions to this exciting area of research. In particular  we would like to thank Dmitry Zubarev and Brooke Tvermoes for helpful conversations during the preparation of this perspective 

主　　题：artificial computing enable 

摘      要：New tools enable new ways of working,and materials science is no *** materials discovery,traditional manual,serial,and human-intensive work is being augmented by automated,parallel,and iterative processes driven by Artificial Intelligence (AI),simulation and experimental *** this perspective,we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery *** show,using the example of the development of a novel chemically amplified photoresist,how these technologies’ impacts are amplified when they are used in concert with each other as powerful,heterogeneous workflows.