Tandem structural transformations from 4-fold to 5-fold interpenetrated Cd(II) metal-organic frameworks

Prediction of Metal-Organic Frameworks with Phase Transition via Machine Learning

Metal-organic frameworks (MOFs) possess a virtually unlimited number of potential structures. Although the latter enables an efficient route to control the structure-related functional properties of MOFs, it still complicates the prediction and searching for an optimal structure for specific application. Next to prediction of the MOFs for gas sorption/separation and catalysis via machine learning (ML), we report on ML to find MOFs demonstrating a phase transition (PT). On the basis of an available QMOF database (7463 frameworks), we create and train the autoencoder followed by training the classifier of MOFs from a unique database with experimentally confirmed PT. This makes it possible to identify MOFs with a high potential for PT and evaluate the most likely stimulus for it (guest molecules or temperature/pressure). The formed list of available MOFs for PT allows us to discuss their structural features and opens an opportunity to search for phase change MOFs for diverse physical/chemical application.

Hetero-interpenetrating porous coordination polymers

Interpenetrating porous coordination polymers (IPCPs) consist of two or more networks that are mechanically interlocked to form a coherent structure. The framework topology and the chemical composition are the two important criteria of interpenetrating networks to distinguish homo-IPCPs from hetero-IPCPs. Compared to rich homo-IPCPs, hetero-IPCPs remain rare and are formed rather unexpectedly. This Frontier article highlights hetero-IPCPs in accordance with the difference and/or identity of individual networks in terms of the framework topology and the chemical composition.