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Artificial intelligence for materials discovery

Published online by Cambridge University Press:  12 July 2019

Carla P. Gomes ,
Bart Selman  and
John M. Gregoire
Carla P. Gomes
Affiliation:
Department of Computer Science, Cornell University, USA; gomes@cs.cornell.edu
Bart Selman
Affiliation:
Department of Computer Science, Cornell University, USA; selman@cs.cornell.edu
John M. Gregoire
Affiliation:
Joint Center for Artificial Photosynthesis, California Institute of Technology, USA; gregoire@caltech.edu
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Abstract

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.

Keywords

simulationelementalx-ray diffraction (XRD)
Type
The Machine Learning Revolution in Materials Research
Information
MRS Bulletin , Volume 44 , Issue 7: The Machine Learning Revolution in Materials Research , July 2019 , pp. 538 - 544
Copyright
Copyright © Materials Research Society 2019 

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