Journal
ADVANCED ENERGY MATERIALS
Volume 10, Issue 8, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/aenm.201903242
Keywords
energy materials; machine learning; materials design
Categories
Funding
- Materials Project - U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division [DE-AC02-05-CH11231, KC23MP]
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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in-depth review of the application of ML to energy materials, including rechargeable alkali-ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.
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