为金属有机框架利用机器学习:一个视角
Hongjian Tang1,2, Lunbo Duan1, Jianwen Jiang2
1Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy & Environment, Southeast University, Nanjing 210096, China.
Langmuir : the ACS journal of surfaces and colloids
|November 3, 2023
概括
机器学习 (ML) 加快了金属有机框架 (MOF) 的发现和设计. ML有效地预测MOF属性,并揭示结构-属性关系,克服传统方法的局限性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 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 (MOFs) 提供可调节的结构和特性,导致超过10万个合成和数百万个假设候选人.
- MOFs的巨大化学空间使得使用传统的实验或模拟方法对特定应用具有挑战性,以确定最佳材料.
研究的目的:
- 审查机器学习 (ML) 对MOF发现,设计和合成的变革性影响.
- 突出ML在预测MOF属性和推导结构-属性关系方面的能力.
- 讨论目前的挑战和未来的机遇,为ML在推进MOF发展.
主要方法:
- 利用机器学习 (ML) 算法进行MOF属性预测.
- 利用丰富的实验和模拟数据用于ML模型训练.
- 在ML驱动的MOF研究中分析数据采集,特色化和模型培训方面.
主要成果:
- 机器学习显著加速了MOF的发现和设计过程.
- ML可以有效和准确地预测MOF属性.
- ML 便于用于合理的 MOF 设计的结构-属性关系的定量推导.
结论:
- 机器学习正在彻底改变金属有机框架的领域.
- ML克服了传统方法在导航广的MOF化学空间的局限性.
- 未来的机器学习探索对加速新型MOFs的开发具有重大前景.
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