Combining Chemical, Geometric, and Novel Topological Features to Develop Generalizable Machine Learning Models for Predicting Mechanically Stable MOFs

Abstract

Metal-organic frameworks (MOFs) are promising functional materials, but poor mechanical stability leading to loss of porosity and degraded performance under external pressure limit their commercial use. The diversity of MOF building blocks makes exhaustive experimental or simulation-based screening for high mechanical stability impractical. While some prior work has used machine learning (ML) to accelerate discovery, ML models typically lack the ability to generalize across diverse MOF topologies. Starting from a dataset with around an order of magnitude more secondary building units and topology types than previously studied, we develop a generalizable and interpretable ML framework to predict MOF mechanical stability (i.e., the bulk modulus). Our ML models incorporate novel and interpretable topological features developed based on principles of net theory and chemical features that are applicable across a broad range of MOF chemistries and topologies. We employ our models in a virtual high-throughput screening of over ~435k MOFs from existing hypothetical and experimental databases to identify the most mechanically stable candidates with potential industrial applications.