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Artificial intelligence for materials discovery

  • The Machine Learning Revolution in Materials Research
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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.

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Authors and Affiliations

  1. Department of Computer Science, Cornell University, USA

    Carla P. Gomes & Bart Selman

  2. Joint Center for Artificial Photosynthesis, California Institute of Technology, USA

    John M. Gregoire

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  1. Carla P. Gomes
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  2. Bart Selman
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  3. John M. Gregoire
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Correspondence to Carla P. Gomes.

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Gomes, C.P., Selman, B. & Gregoire, J.M. Artificial intelligence for materials discovery. MRS Bulletin 44, 538–544 (2019). https://doi.org/10.1557/mrs.2019.158

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