Artificial intelligence for materials discovery
Creators
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
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2019-08-01Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field