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通过机器学习加速金属有机框架材料的发现和优化

Hong Wang¹, Liang Yang², Deying Leng²

¹School of Physics and Electronic Information, Yan'an University, Yan'an 716000, China; Key Laboratory of Advanced Optoelectronic Materials and Devices of Higher Education Institutions in Shaanxi, Yan'an 716000, China.

Advances in colloid and interface science

|September 16, 2025

概括

机器学习 (ML) 加快了金属有机框架 (MOF) 的发现和优化,这是关键的多孔材料. 本综述详细介绍了ML算法及其在MOF研究中的应用,以更快地开发材料.

科学领域:

材料科学材料科学材料科学
计算化学的计算化学
化学工程是化学工程的重要组成部分.

背景情况:

金属有机框架 (MOF) 是先进的多孔材料,具有多样化的应用.
传统的MOF合成和优化速度缓慢且昂贵.
机器学习 (ML) 提供了一种有希望的方法来克服这些局限性.

研究的目的:

系统地审查ML在MOF研究中的应用.
涵盖ML算法,数据处理,模型评估和可解释性.
突出ML在加速MOF设计,选和性能预测中的作用.

主要方法:

复习常见的ML算法 (回归,分类,集群,深度学习,强化学习).
讨论数据采集,预处理和模型评估技术.
在MOF材料设计和性能预测中分析ML应用.

主要成果:

ML显著加快了MOF的研究和开发.
ML与MOF科学的整合提供了一个全面的视角.
ML有助于结构与属性关系分析和性能预测.

结论:

机器学习对于推动MOF研究,设计和应用至关重要.
跨学科的合作对于未来的进步至关重要.
机器学习工具可以指导实验科学家,促进绿色化学和可持续发展.

关键词:

机器学习是机器学习.材料设计材料设计金属有机框架的框架.性能预测性能预测的预测.

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