Thermal Stability of Metal–Organic Frameworks (MOFs): Concept, Determination, and Model Prediction Using Computational Chemistry and Machine Learning

Abstract

The indubitable rise of metal−organic framework (MOF) technology has opened the potential for commercialization as alternative materials with a versatile number of applications that range from catalysis to greenhouse gas capture. However, there are several factors that constrain the direct scale-up of MOFs from laboratory to industrial plant given the insufficient knowledge about the overall safety in synthesis processes. This article focuses on the study of MOF thermal stability, from concept to prediction, and the factors that influence such stability. The core of this work is a thermal stability prediction model for MOFs. This model can be applied to existing and new MOF structures, and it will allow for an estimation of the thermal stability temperature range of MOFs. This work contributes to the overall advancement of MOF technology and the efforts for its commercial use at industrial scale, combining both experimental data and computational techniques.

Facile, fast and green synthesis of a highly porous calcium-syringate bioMOF with intriguing triple bioactivity

“…2 For adsorption-related applications, thermal stability plays an important role in ensuring the integrity of the MOF during the interaction with the adsorbate, as well as in the regeneration process of the material for further reuse. 26 Here, the thermal stability of CaSyr-1 was evaluated through thermogravimetric analysis (TGA) under N2 flow (Fig. S8).…”

Metal–organic framework clustering through the lens of transfer learning

“…Cluster analysis has recently been used for MOFs with some success. Escobar-Hernandez and coworkers used k -means clustering to evaluate MOF models that deal with thermal stability, 56 Rosen et al employed UMAP to determine quantum properties in MOFs, 57 and Wu et al used UMAP and k -means to condense MOF features into an accessible representation of the space. 58…”

Automated Graph Neural Networks Accelerate the Screening of Optoelectronic Properties of Metal–Organic Frameworks

“…Metal–organic frameworks (MOF) are synthesized from metal ions and organic linkers through strong bonding with a large amount of experimentally reported structures. − Machine learning (ML) approaches have been used to evaluate the performance and accelerate the discovery of advanced MOFs in a time- and cost-efficient manner using density functional theory (DFT) simulations . ML-assisted screening has been successfully employed in the prediction of gas sorption and separation, − catalysis, , thermal stability, , and photoelectric properties. , Compared with ML models such as kernel ridge regression (KRR), random forest, and decision tree, the graph neural networks (GNNs) have higher accuracy and prediction performance. Similar to organic–inorganic hybrid perovskites, − numerous MOFs with organic and inorganic components have enormous potential in solar cells.…”

“…1−6 Machine learning (ML) approaches have been used to evaluate the performance and accelerate the discovery of advanced MOFs in a time-and cost-efficient manner using density functional theory (DFT) simulations. 7 ML-assisted screening has been successfully employed in the prediction of gas sorption and separation, 8−11 catalysis, 12,13 thermal stability, 14,15 and photoelectric properties. 16,17 Compared with ML models such as kernel ridge regression (KRR), 16 random forest, 18 and decision tree, 19 the graph neural networks (GNNs) have higher accuracy and prediction performance.…”