利用热能的机器学习：从材料发现到系统优化,ACS Energy Letters

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利用热能的机器学习：从材料发现到系统优化
ACS Energy Letters ( IF 19.3 ) Pub Date : 2022-09-01 , DOI: 10.1021/acsenergylett.2c01836
Man Li ₁ , Lingyun Dai ₁ , Yongjie Hu ₁

Affiliation

机器学习 (ML) 的最新进展对基于统计视角的研究界产生了影响，并从传统角度揭示了不可见的事物。尽管该领域仍处于早期阶段，但这一进展已推动热科学和工程界应用此类尖端工具集来分析复杂数据、揭示深奥模式和发现非直观原理。在这项工作中，我们全面概述了机器学习方法在热能研究关键主题上的应用和未来机遇，从自下而上的材料发现到跨原子级到多尺度的自上而下的系统设计。特别是，我们专注于一系列令人印象深刻的机器学习工作，研究最先进的热传输模型（密度泛函理论、分子动力学、和玻尔兹曼输运方程）、不同系列的材料（半导体、聚合物、合金和复合材料）、热性能的各个方面（电导率、发射率、稳定性和热电）以及工程预测和优化（设备和系统）。我们讨论了当前机器学习方法的前景和挑战，并为未来的方向和新算法提供了前景，这些可能会对热能研究产生进一步的影响。

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Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System 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.

更新日期：2022-09-01

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