ReDD-COFFEE under the lens: revealing adsorption and separation performances of hypothetical COFs using molecular simulations and machine learning

Abstract

In this work, we performed a high-throughput computational screening approach combining Grand Canonical Monte Carlo (GCMC) simulations and machine learning (ML) to unlock the potential of the ReDD-COFFEE (Ready-to-use and Diverse Database of Covalent Organic Frameworks with Force field-based Energy Evaluation) database for gas adsorption and separation applications. Molecular simulations were first employed to assess CO2, CH4, H-2, N-2 and O-2 uptakes of acylhydrazone-, azine-, and triazine-based hypothetical COFs (hypoCOFs). These data were then leveraged to train ML models capable of predicting adsorption properties for nearly 25000 different types of materials. Adsorption selectivities of ReDD-hypoCOFs were computed for six important gas separations: CO2/CH4, CO2/H-2, CO2/N-2, CH4/H-2, CH4/N-2, and O-2/N-2. Structure-performance analyses performed using molecular fingerprinting on top-selective materials demonstrated that nitrogen enriched aromatic rings and fluorinated linkers in addition to narrow pores (<10 & Aring;) and low porosities (<0.7) collectively strengthen the CO2 affinity of ReDD-hypoCOFs.