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Glass formation in Zeolitic Imidazolate Frameworks (ZIFs) has garneredsignificant attention in the field of Metal-Organic Frameworks (MOFs) in recentyears. Numerous works have been conducted to investigate the microscopicmechanisms involved in the melting-quenching process of ZIFs. Understanding thedensity variations that occur during the melting process of ZIFs is crucial forcomprehending the origins of glass formation. However, conducting large-scalesimulations has been challenging due to limitations in computational resources.In this work, we utilized deep learning methods to accurately construct apotential function that describes the atomic-scale melting behavior of ZeoliticImidazolate Framework-4 (ZIF-4). The results revealed the spatial heterogeneityassociated with the formation of low-density phases during the melting processof ZIF-4. This work discusses the advantages and limitations of applying deeplearning simulation methods to complex structures like ZIFs, providing valuableinsights for the development of machine learning approaches in designingMetal-Organic Framework glasses.

Unraveling Medium-Range Order and Melting Mechanism of ZIF-4 under High Temperature