Artificial intelligence for materials discovery

被引:65
|
作者
Gomes, Carla R. [1 ]
Selman, Bart [1 ]
Gregoire, John M. [2 ]
机构
[1] Cornell Univ, Dept Comp Sci, Ithaca, NY 14853 USA
[2] CALTECH, Joint Ctr Artificial Photosynth, Pasadena, CA 91125 USA
关键词
simulation; elemental; x-ray diffraction (XRD); DEEP NEURAL-NETWORKS; GO; GAME;
D O I
10.1557/mrs.2019.158
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Continued progress in artificial intelligence (AI) and associated demonstrations of superhuman performance have raised the expectation that AI can revolutionize scientific discovery in general and materials science specifically. We illustrate the success of machine learning (ML) algorithms in tasks ranging from machine vision to game playing and describe how existing algorithms can also be impactful in materials science, while noting key limitations for accelerating materials discovery. Issues of data scarcity and the combinatorial nature of materials spaces, which limit application of ML techniques in materials science, can be overcome by exploiting the rich scientific knowledge from physics and chemistry using additional AI techniques such as reasoning, planning, and knowledge representation. The integration of these techniques in materials-intelligent systems will enable AI governance of the scientific method and autonomous scientific discovery. © 2019 Materials Research Society.
引用
收藏
页码:538 / 544
页数:7
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