MATERIALS VIRTUAL LAB

Machine learning interatomic potentials (MLIPs) are powerful tools for atomistic simulations, but training them with high-fidelity quantum mechanical data is costly. Most MLIPs rely on low-cost PBE calculations, while more accurate SCAN functionals are computationally expensive... In this work, Tsz Wai Ko introduces a multi-fidelity M3GNet approach that can achieve SCAN-level accuracy with 10% SCAN data, reducing computational costs significantly... Efficiency: Cuts high-fidelity data requirements by up to 90% while improving accuracy.