机器学习利用热能:从材料发现到系统优化
Man Li1, Lingyun Dai1, Yongjie Hu1
1Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States.
ACS energy letters
|June 16, 2023
概括
机器学习 (ML) 通过分析用于材料发现和系统设计的复杂数据来加速热科学研究. 本概述探讨了ML在热传输,材料,性能和工程中的应用,并突出了未来的机遇.
科学领域:
- 热科学与工程 热科学与工程
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 机器学习 (ML) 提供了新的统计方法来分析复杂的数据.
- 传统方法在揭示热科学中复杂的模式方面面临局限性.
- 机器学习领域正在迅速发展,为科学发现提供了新的机会.
研究的目的:
- 在热能研究中提供ML应用的全面概述.
- 探索ML在材料发现和系统设计中的潜力.
- 讨论当前的挑战和未来的方向在热科学ML.
主要方法:
- 在各种尺度上对ML应用的审查,从原子到多尺度.
- 专注于热传输建模中的ML (DFT,MD,BTE).
- 对各种材料家族和热性质的ML进行检查.
主要成果:
- ML正在应用于半导体,聚合物,合金和复合材料.
- ML有助于预测导热率,发射率,稳定性和热电性.
- 在工程预测和热设备和系统的优化方面,ML显示出前景.
结论:
- ML为推进热能研究提供了重大机会.
- 应对当前的挑战和开发新的算法将对未来的影响至关重要.
- 机器学习的集成可以导致非直观的发现和改进的热管理.
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