







@article{
doi:10.26434/chemrxiv.15001161/v1,
author = {Carles Bo  and Gabriela Dias Da Silva  and Farzaneh Hosseini  and Ajmal Rahman Mullukkandy  and Hugo Salazar-Lozas  and Albert Solé-Daura  and Yingying Wang },
title = {UMA-ASE: A FAIR-Aware Environment for Machine-Learned Interatomic Potential Workflows with UMA and ASE},
journal = {ChemRxiv},
volume = {2026},
number = {0323},
pages = {},
year = {2026},
doi = {10.26434/chemrxiv.15001161/v1},
URL = {https://chemrxiv.org/doi/abs/10.26434/chemrxiv.15001161/v1},
eprint = {https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15001161/v1},
abstract = {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.}}

