期刊
ACS ENERGY LETTERS
卷 7, 期 10, 页码 3204-3226出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsenergylett.2c01836
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资金
- CAREER Award from the National Science Foundation (NSF) [DMR-1753393]
- Alfred P. Sloan Research Fellowship [FG-2019-11788]
- NIGMS Research Award [R35GM147391]
- Sustainable LA Grand Challenge
- Anthony and Jeanne Pritzker Family Foundation
- Extreme Science and Engineering Discovery Environment [ACI-1548562]
- NSF [DMR180111]
- Vernroy Makoto Watanabe Excellence in Research Award
- NSF [DMR180111]
This article provides an overview of the applications and future opportunities of machine learning methods in thermal energy research. It covers a wide range of topics, including thermal transport modeling, different materials, thermal properties, and engineering prediction and optimization.
Recent advances in machine learning (ML) have impacted research communities based on statistical perspectives and uncovered invisibles from conventional standpoints. Though the field is still in the early stage, this progress has driven the thermal science and engineering communities to apply such cutting-edge toolsets for analyzing complex data, unraveling abstruse patterns, and discovering non-intuitive principles. In this work, we present a holistic overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales. In particular, we focus on a spectrum of impressive ML endeavors investigating the state-of-the-art thermal transport modeling (density functional theory, molecular dynamics, and Boltzmann transport equation), different families of materials (semiconductors, polymers, alloys, and composites), assorted aspects of thermal properties (conductivity, emissivity, stability, and thermoelectricity), and engineering prediction and optimization (devices and systems). We discuss the promises and challenges of current ML approaches and provide perspectives for future directions and new algorithms that could make further impacts on thermal energy research.
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