Exploring metal-organic framework phase change materials via machine learning approach

This study focuses on using an artificial intelligence to explore metal-organic framework (MOFs) supporting the structural transformations (for instance, phase change, structural breathing, and crystal-to-crystal phase transition). Since the most MOFs possess flexible and adaptive structure, they are widely used as smart materials for optical keys, triggers, switchers, and even information encrypts. However, 100.000 potential MOFs are strongly complicated the search of specific MOF for targeted applications. Here, we report on a unique database of MOFs demonstrating the structural transformation occurring between different space groups or crystal symmetries. Using a autoencoder and classifier to predict the structural transformations, we build a link between the initial MOF structure and the potential to be switched.* The results pave the way to predict and design an efficient phase change MOFs for potential application in optical data processing and storage.