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机器学习帮助设计和优化热元材料的设计和优化
Changliang Zhu1, Emmanuel Anuoluwa Bamidele2, Xiangying Shen1
1Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, P.R. China.
Chemical reviews
|March 28, 2024
概括
人工智能 (AI) 和机器学习 (ML) 通过预测性能和优化结构来加速热元材料设计. 本综述涵盖了AI方法和热工程中的案例研究.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 人工智能的人工智能
背景情况:
- 传统的材料研究在探索复杂的热性能方面面临着局限性.
- 机器学习 (ML) 提供了新的方法来预测和设计具有特定热特性的材料.
- 热元材料由于其复杂的结构和所需的功能,提出了独特的设计挑战.
研究的目的:
- 审查人工智能 (AI) 和ML在热元材料的设计和优化中的应用.
- 在这个领域阐明歧视性和生成性模型的方法.
- 讨论AI与优化算法的集成,以提高效率.
主要方法:
- 对歧视性和生成性AI模型的解释.
- 概述与AI一起使用的优化算法.
- 介绍了在热元材料设计中展示ML应用的案例研究.
主要成果:
- 人工智能,特别是机器学习,可以预测前所未有的热性能.
- 区分模型与优化算法的整合提高了计算效率.
- 生成型模型促进了热元材料的结构设计和优化.
结论:
- 人工智能和机器学习是推动热元材料研究的变革性工具.
- 需要进一步发展,以应对挑战,并利用这个快速发展的领域的机遇.
- 该审查强调了人工智能的潜力,可以彻底改变具有量身定制热性能的材料设计.
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