机器学习和人工智能赋权金属有机框架:合成,性能预测和治疗应用
Ran Chen1, Yuanwei He1, Zitao Chen1
1Dongguan Key Laboratory of Drug Design and Formulation Technology, School of Pharmacy, Guangdong Medical University, Dongguan, 523808, China. panying@gdmu.edu.cn.
Dalton transactions (Cambridge, England : 2003)
|January 26, 2026
概括
机器学习 (ML) 是人工智能 (AI) 的一个子集,通过实现高通量选和属性预测来加速金属有机框架 (MOF) 研究. 本综述涵盖了ML机制,MOF数据库,以及在合成,药物加载,气体吸附和疾病诊断中的应用.
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:
- 金属有机框架 (MOF) 具有巨大的潜力,但由于大型结构图书馆而面临研究瓶.
- 传统的试错实验不足以探索MOF属性的全部范围.
- 机器学习 (ML) 为高效的MOF研究提供数据驱动的解决方案.
研究的目的:
- 审查ML和MOF数据库的机制.
- 突出 ML 在 MOF 研究中的近期应用.
- 讨论ML在MOF科学中的挑战和未来方向.
主要方法:
- 在MOF研究中对ML应用现有文献的审查.
- 讨论ML算法及其与MOF属性预测的相关性.
- 分析用于ML模型培训和验证的MOF数据库.
主要成果:
- ML使MOF的高通量选成为可能.
- ML准确地预测MOF的特性,如合成路径,晶体结构,药物加载和气体吸附.
- ML显示了使用MOF相关数据诊断疾病的新兴应用.
结论:
- 机器学习对于克服MOF研究的局限性和加速材料发现至关重要.
- 通过ML了解MOF特征和属性之间的关系可以提高研究效率.
- 对ML算法的进一步研究将促进ML在MOF中的实际应用.
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