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Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics

Published online by Cambridge University Press:  22 July 2019

Rama K. Vasudevan Open the ORCID record for Rama K. Vasudevan [Opens in a new window] ,
Kamal Choudhary ,
Apurva Mehta ,
Ryan Smith ,
Gilad Kusne ,
Francesca Tavazza ,
Lukas Vlcek ,
Maxim Ziatdinov ,
Sergei V. Kalinin  and
Jason Hattrick-Simpers
Rama K. Vasudevan*
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Kamal Choudhary
Affiliation:
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Apurva Mehta
Affiliation:
Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
Ryan Smith
Affiliation:
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Gilad Kusne
Affiliation:
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Francesca Tavazza
Affiliation:
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Lukas Vlcek
Affiliation:
Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Maxim Ziatdinov
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Sergei V. Kalinin
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Jason Hattrick-Simpers
Affiliation:
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
*
Address all correspondence to Rama K. Vasudevan at [email protected]
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Abstract

The use of statistical/machine learning (ML) approaches to materials science is experiencing explosive growth. Here, we review recent work focusing on the generation and application of libraries from both experiment and theoretical tools. The library data enables classical correlative ML and also opens the pathway for exploration of underlying causative physical behaviors. We highlight key advances facilitated by this approach and illustrate how modeling, macroscopic experiments, and imaging can be combined to accelerate the understanding and development of new materials systems. These developments point toward a data-driven future wherein knowledge can be aggregated and synthesized, accelerating the advancement of materials science.

Type
Artificial Intelligence Prospectives
Information
MRS Communications , Volume 9 , Issue 3 , September 2019 , pp. 821 - 838
Copyright
Copyright © The Author(s) 2019 

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