GFN1-xTB-Assisted Machine Learning for Electronic Structure Screening of Metal–Organic Frameworks

Abstract

Metal–organic frameworks (MOFs) are versatile materials with tunable crystal structures, morphologies, and chemistries, offering diverse physical and chemical properties. Although typically electrically insulating, specific combinations of organic and inorganic components can impart electrical conductivity to MOFs. The virtually limitless chemical space of MOFs, however, presents a significant challenge in identifying optimal candidates for various applications. Although density functional theory (DFT) can probe their electronic structure, its high computational cost hinders the discovery of novel electroactive MOFs using machine learning due to limited data. To tackle these challenges, a semiempirical extended tight-binding approach (GFN1-xTB) is employed to compute the electronic properties of a dataset of MOFs, and it is shown that GFN1-xTB approximates MOF band gaps well, as compared to semilocal DFT. These data are used to train an interpretable Δ-learning model that predicts the difference between low- and high-fidelity band gaps, given by xTB and DFT data at the hybrid level, respectively. This model outperforms direct models trained using only the DFT values. The Δ-learning model also outperforms models with deep-learning architecture, fine-tuned on our custom dataset to predict the band gaps of MOFs. With limited high-quality DFT band gaps, taking advantage of Δ-learning using low-cost GFN1-xTB leads to better predictions than relying on DFT data alone.