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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

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.

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Acknowledgments

The work was supported by the U.S. Department of Energy, Office of Science, Materials Sciences and Engineering Division (R.K.V. and S.V.K.). A portion of this research was conducted at and supported (M.Z.) by the Center for Nanophase Materials Sciences, which is a US DOE Office of Science User Facility.

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Vasudevan, R.K., Choudhary, K., Mehta, A. et al. Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics. MRS Communications 9, 821–838 (2019). https://doi.org/10.1557/mrc.2019.95

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