机器学习设计金属有机框架:从数据效率角度看的进展和挑战
Diego A Gómez-Gualdrón1,2, Tatiane Gercina de Vilas1, Katherine Ardila1
1Department of Chemical and Biological Engineering, Colorado School of Mines, 1601 Illinois St, Golden, CO 80401, USA. dgomezgualdron@mines.edu.
Materials horizons
|December 15, 2025
概括
机器学习 (ML) 通过克服设计挑战,加速金属有机框架 (MOF) 的发现. 本综述探讨了用于MOF属性预测和设计的高效ML策略,提高了研究人员的可访问性.
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
- 人工智能的人工智能
背景情况:
- 金属有机框架 (MOF) 提供了设计灵活性,但提供了广的搜索空间,使传统选方法资源密集.
- 越来越多的MOF数据需要高效的计算方法来加速发现和设计.
研究的目的:
- 在ML和MOF的交叉点批判性地审查机器学习 (ML) 应用程序.
- 在MOF物业预测和设计中调查减少数据和资源负担的策略.
- 为了确定 ML 授权的 MOF 设计的未来机会.
主要方法:
- 功能工程的特点工程.
- 模型架构选择模型架构选择
- 转移学习转移学习
- 积极学习是指积极学习.
- 生成型模型是一种生成型模型.
主要成果:
- 机器学习方法提供了一个有希望的解决方案,通过解决大型设计空间来加速MOF发现.
- 包括特征工程和生成模型在内的各种策略可以提高数据和资源效率.
- 目前的ML应用主要集中在MOF吸附特性上,具有更广泛应用的潜力.
结论:
- 有效的ML策略对于强大且可访问的ML辅助MOF设计至关重要.
- 解决数据质量和可扩展性挑战将进一步推动MOF研究中的ML.
- 未来的工作应该将ML应用扩展到吸附性质以外的其他MOF功能.
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