Machine learning in nuclear materials research

Morgan, Dane; Pilania, Ghanshyam; Couet, Adrien; Uberuaga, Blas P.; Sun, Cheng; Li, Ju

doi:10.1016/j.cossms.2021.100975

Title: Machine learning in nuclear materials research

Journal Article · 14 January 2022 · Current Opinion in Solid State and Materials Science

DOI: https://doi.org/10.1016/j.cossms.2021.100975 · OSTI ID:1846921

Morgan, Dane ^[1];

^[2]; Couet, Adrien ^[1];

^[2];

^[3]; Li, Ju ^[4]

Univ. of Wisconsin, Madison, WI (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: 89233218CNA000001; AC07-05ID14517; AR0001050; NE0008678; NE0008827

OSTI ID:: 1846921

Report Number(s):: INL/JOU-21-62819-Rev000; LA-UR-21-32380

Journal Information:: Current Opinion in Solid State and Materials Science, Journal Name: Current Opinion in Solid State and Materials Science Journal Issue: 2 Vol. 26; ISSN 1359-0286

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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