Published July 2019 | Version public
Journal Article

Artificial intelligence for materials discovery

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.

Additional Information

© 2019 Materials Research Society. Published online by Cambridge University Press: 12 July 2019. This work was supported by an award from the Toyota Research Institute; NSF Award Nos. CCF-1522054 and CNS-0832782 (Expeditions), CNS-1059284 (Infrastructure), and IIS-1344201 (INSPIRE); ARO Award No. W911-NF-14-1-0498; AFOSR Multidisciplinary University Research Initiatives (MURI) Program FA9550-18-1-0136; and US DOE Award No. DE-SC0004993.

Additional details

Identifiers

Eprint ID
97575
Resolver ID
CaltechAUTHORS:20190801-085714793

Funding

Toyota Research Institute
NSF
CCF-1522054
NSF
CNS-0832782
NSF
CNS-1059284
NSF
IIS-1344201
Army Research Office (ARO)
W911-NF-14-1-0498
Air Force Office of Scientific Research (AFOSR)
FA9550-18-1-0136
Department of Energy (DOE)
DE-SC0004993

Dates

Created
2019-08-01
Created from EPrint's datestamp field
Updated
2021-11-16
Created from EPrint's last_modified field

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