Accelerating melting temperature predictions by leveraging LASP machine learning potentials in the SLUSCHI package

Abstract The automated computational package SLUSCHI , originally interfaced with the first‐principles package VASP , has demonstrated effectiveness but remains computationally demanding for accurately calculating melting temperatures. This study leverages machine learning potentials via the efficient molecular dynamics simulator LAMMPS , utilizing pre‐trained LASP neural network potentials derived from first‐principles data. Tests on 30 diverse material systems—including simple metals, transition metals, alloys, oxides, and carbides—demonstrate that this approach significantly cuts computational costs, often by more than one order of magnitude compared to conventional DFT simulations. Approximately 60% of the calculated melting temperatures, prior to applying any DFT‐based correction, fall within 200 K of experimental values. Focusing specifically on single‐element systems, where direct comparison with DFT is possible, the percentage of melting temperatures within 200 K of experimental data improves from 53% to 82% following a DFT correction. This substantial improvement in computational efficiency, without sacrificing accuracy, facilitates high‐throughput materials screening and accelerates material design consistent with the materials genome paradigm.