机器学习伴以化学直觉：快速筛选储气材料

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Chemically intuited, large-scale screening of MOFs by machine learning techniques

(通过机器学习技术辅以化学直觉，作大规模金属-有机框架材料筛选)

Giorgos Borboudakis,  Taxiarchis Stergiannakos,  Maria Frysali,  Emmanuel Klontzas,  Ioannis Tsamardinos & George E. Froudakis

A novel computational methodology for large-scale screening of Metal–organic frameworks (MOFs) is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.

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