CoRE MOF DB: A Curated Experimental Metal-Organic Framework Database with Machine-Learned Properties for Integrated Material-Process Screening

Abstract

We present an updated version of the CoRE MOF database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. Data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of a MOF structure. DDEC06 partial atomic charges of MOFs were assigned based on a machine learning model. Gibbs-Ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications.

Keywords: metal-organic framework, material database, multi-scale modeling, carbon dioxide capture