  Glass formation in Zeolitic Imidazolate Frameworks (ZIFs) has garnered
significant attention in the field of Metal-Organic Frameworks (MOFs) in recent
years. Numerous works have been conducted to investigate the microscopic
mechanisms involved in the melting-quenching process of ZIFs. Understanding the
density variations that occur during the melting process of ZIFs is crucial for
comprehending the origins of glass formation. However, conducting large-scale
simulations has been challenging due to limitations in computational resources.
In this work, we utilized deep learning methods to accurately construct a
potential function that describes the atomic-scale melting behavior of Zeolitic
Imidazolate Framework-4 (ZIF-4). The results revealed the spatial heterogeneity
associated with the formation of low-density phases during the melting process
of ZIF-4. This work discusses the advantages and limitations of applying deep
learning simulation methods to complex structures like ZIFs, providing valuable
insights for the development of machine learning approaches in designing
Metal-Organic Framework glasses.
