Machine learning prediction of thermodynamic stability and electronic properties of 2D layered conductive metal-organic frameworks

Two-dimensional layered electrically conductive metal-organic frameworks (EC MOFs) have emerged as promising materials for electronic and energy applications due to their tunable electronic properties and structural versatility. Here, we employ machine learning (ML) models to predict the thermodynamic stability and electronic properties of EC MOFs, significantly reducing the dependence on costly ab initio calculations. We construct different feature sets by integrating compositional features from generic statistical reduction methods along with structural descriptors curated from the EC-MOF database (termed GD, M-GD, and A-GD features). Various ML models, including linear, tree-based, and ensemble learning approaches, are benchmarked for predicting formation energies of EC MOFs, as one measure of synthesizability, as well as predicting their electrically conductive nature. The density functional theory data pool spans the 536 monolayer systems in the EC-MOF database, divided into 90% training and 10% test sets. Our results demonstrate that ML models treated by proper feature engineering can achieve very high accuracy for formation energy prediction, with the coefficient of determination, R², being as high as 0.96. Via proper feature engineering, we also report up to 92% accuracy in predicting the metallicity of EC MOFs and 82% in bandgap classification using the extra tree classifier. The trained ML models are further applied to a new class of EC MOFs, which are neither part of the training nor members of the EC-MOF database, showcasing their predictive power and transferability. This work establishes the foundations of a data-driven framework for accelerating the discovery of novel EC MOFs in future research.