Accelerated Discovery of Materials with Extreme Work Functions through Uncertainty-Aware Multi-Fidelity Screening

Abstract

Work function plays a pivotal role in technologies ranging from energy conversion and electronics to catalysis. In this work, we integrated machine learning (ML) with multi-fidelity screening to develop a data-driven framework for accelerating the discovery of materials with extreme work functions. We augmented a previously published Random Forest (RF) model for work function to include prediction uncertainty calibration and domain of applicability assessment to enhance prediction robustness. By combining the augmented RF model with universal ML interatomic potential simulations and targeted ab initio calculations, we screened 5.5 million compounds from the GNoME and Alexandria databases. This workflow identified 209 surfaces with extreme low work functions below 2.0 eV and 227 surfaces with extreme high work functions above 6.0 eV, corresponding to 136 and 172 unique materials, respectively. The resulting candidates revealed trends consistent with established chemical principles, including the tendency of alkali- and alkaline-earth-terminated surfaces to exhibit low work functions. While it also uncovered less conventional motifs: lanthanide-rich surface terminations were strongly associated with extremely low work functions, whereas surfaces containing metalloids or phosphorus at the top layer were correlated with exceptionally high work functions. This work demonstrates a scalable strategy that leverages ML models and multi-fidelity computational efforts to accelerate the discovery of materials with extreme work functions for advanced electronic, energy-conversion, and catalytic applications.