UMA-ASE: A FAIR-Aware Environment for Machine-Learned Interatomic Potential Workflows with UMA and ASE

Machine-learned interatomic potentials (MLIPs) are rapidly changing the operating regime of computational chemistry by narrowing the traditional gap between first-principles accuracy and large-scale atomistic simulation. UMA-ASE was developed to make this transition operational through a reproducible, FAIR-aware environment that couples the Universal Model for Atoms (UMA) with the Atomic Simulation Environment (ASE) and exposes the resulting workflows through both a command-line interface and a browser-based application. The platform combines structure intake and generation from SMILES, job submission, GeoOpt and TS search, molecular dynamics, vibrational analysis, visualization, post-processing, queue management, and integration with ioChem-BD. In this manuscript we position UMA-ASE within the current MLIP landscape and define the design goals of the new software. We emphasize reproducibility artifacts such as structured logs, machine-readable .use recipes, result bundles, and repository-oriented interoperability. We conclude that FAIR-aware scientific tools and user-friendly interfaces are becoming essential complements to modern ML methods, and that LLM-assisted software development is likely to accelerate this trend further.