A review on machine learning-guided design of energy materials

Kim, Seongmin; Xu, Jiaxin; Shang, Wenjie; Xu, Zhihao; Lee, Eungkyu; Luo, Tengfei

doi:10.1088/2516-1083/ad7220

Title: A review on machine learning-guided design of energy materials

Journal Article · 03 September 2024 · Progress in Energy

DOI: https://doi.org/10.1088/2516-1083/ad7220 · OSTI ID:2439497

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; Shang, Wenjie;

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Abstract The development and design of energy materials are essential for improving the efficiency, sustainability, and durability of energy systems to address climate change issues. However, optimizing and developing energy materials can be challenging due to large and complex search spaces. With the advancements in computational power and algorithms over the past decade, machine learning (ML) techniques are being widely applied in various industrial and research areas for different purposes. The energy material community has increasingly leveraged ML to accelerate property predictions and design processes. This article aims to provide a comprehensive review of research in different energy material fields that employ ML techniques. It begins with foundational concepts and a broad overview of ML applications in energy material research, followed by examples of successful ML applications in energy material design. We also discuss the current challenges of ML in energy material design and our perspectives. Our viewpoint is that ML will be an integral component of energy materials research, but data scarcity, lack of tailored ML algorithms, and challenges in experimentally realizing ML-predicted candidates are major barriers that still need to be overcome.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2439497

Journal Information:: Progress in Energy, Journal Name: Progress in Energy Journal Issue: 4 Vol. 6; ISSN 2516-1083

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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