SLUSCHI-UP: A Web Infrastructure for SLUSCHI Melting-Temperature Calculations Using Universal Machine-Learning Interatomic Potentials

Abstract

Melting temperature is a critical property for high-temperature materials design, but first-principles melting calculations based on finite-temperature molecular dynamics can require substantial computational resources. The SLUSCHI method reduces this cost by using small-cell solid--liquid coexistence simulations and statistical analysis of many short molecular-dynamics trajectories. Here I present SLUSCHI-UP, a deployed web service for atomistic melting-temperature estimation that couples the SLUSCHI workflow to selectable pretrained universal machine-learning interatomic potentials (uMLIPs) and asynchronous GPU execution. Users submit a crystal structure through a Materials Project identifier or POSCAR input, select a uMLIP backend, and launch a queued melting calculation without local installation of simulation software. The current production interface supports mace-mpa-0-medium, Allegro-OAM-L, and DPA-3.2-5M-OMat24, while beta deployments expose additional models. On the compact MeltBench-10 validation set, the three production backends produce raw coexistence mean absolute errors in the range of approximately 178--327 K. Across the broader set of materials tested so far in MeltBench, the current deployed-job snapshot contains 119 raw uMLIP entries, and PBE-corrected Allegro-OAM-L predictions reach a mean absolute error of approximately 166 K. These values should be interpreted as screening-level infrastructure validation rather than a definitive ranking of uMLIPs. The results demonstrate that SLUSCHI-UP provides a practical, provenance-aware deployment layer between fast scalar melting-temperature predictors and much more expensive first-principles coexistence calculations, while retaining the usual limitations of uMLIP transferability, finite-size sampling, and high-temperature trajectory stability.