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

TL;DR

This paper employs deep learning to simulate and analyze the atomic-scale melting behavior of ZIF-4, revealing heterogeneity and low-density phase formation, advancing understanding of MOF glass formation.

Contribution

It introduces a deep learning-based potential function for large-scale simulation of ZIF-4 melting, addressing computational challenges and providing new insights into the melting mechanism.

Findings

Heterogeneous spatial distribution during melting

Formation of low-density phases in ZIF-4

Deep learning enhances simulation of complex MOFs

Abstract

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.