机器学习用于纳米孔材料的热传输预测:进展,挑战和机遇
Amirehsan Ghasemi1, Murat Barisik1,2
1Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996, USA.
Nanomaterials (Basel, Switzerland)
|November 12, 2025
概括
机器学习模型可以预测纳米孔材料的热性能,如MOF和热质岩. 本综述指导研究人员使用这些模型进行高效的材料设计和隔热应用.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 预测纳米孔材料的热性能对于隔热和储能应用至关重要.
- 纳米孔状材料,如共价有机框架,金属有机框架,气凝和热质石是这些应用中的关键.
- 当前的预测方法在准确性和效率方面面临挑战.
研究的目的:
- 审查机器学习 (ML) 模型的应用,以预测纳米孔质材料的热性能.
- 讨论ML模型的进步,重点关注预测准确性和计算效率.
- 为研究人员提供关于在纳米孔状材料研究和设计中利用ML的指南.
主要方法:
- 关于纳米孔质材料中ML应用的文献综述.
- 讨论各种ML模型,包括卷积神经网络,图形神经网络和基于物理的神经网络.
- 探索新兴方法,如多式联络和转移学习,以及可解释的ML.
主要成果:
- 机器学习模型在预测热性质方面表现有前途,精度和效率都有所提高.
- 局限性包括数据可用性,保持物理一致性,以及在物质家族中进行概括.
- 新兴的技术,如多式联络和转移学习,可以降低计算成本.
- 可解释的ML方法提供了对潜在的物理机制的洞察.
结论:
- 机器学习为预测和设计具有所需热性能的纳米孔状材料提供了强大的工具.
- 解决数据和模型通用性的局限性是更广泛采用的关键.
- 可解释的ML增强了理解,并促进了用于热管理和储能的新材料的开发.
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