Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
Once trained, machine-learned interatomic potentials (MLIPs) provide a fast and accurate way to study catalytic reaction pathways, but their performance strongly depends on the training set. Here, we compare nine MLIPs trained with different data sets and strategies, including from-scratch (FS) training and fine-tuning (FT) of large foundation models. The models are evaluated on reaction energies, $E_{r}$, and reaction energy barriers, $E_{a}$, for 141 reactions, including CO$_2$ reduction to C$_2$ and C$_3$ products, propane dehydrogenation, hydrogen intercalation on Pd, and out-of-distribution oxygen evolution reaction (OER) on metal oxides. FS models trained with 5%--10% perturbed high-energy configurations from molecular dynamics or contour exploration reduce the error by more than twofold compared with models trained only on relaxation trajectories. In contrast, FT MLIPs are less sensitive to sampling and transfer well to out-of-distribution reactions. An MLIP fine-tuned on metallic catalysts achieves a 0.30 eV MAE for OER on iridium oxide polymorphs, outperforming out-of-the-box MACE-MH-1 by 0.08 eV and the best FS model by 0.14 eV. A model fine-tuned to O and OH adsorption on metal oxides gives a 0.19 eV reaction-barrier MAE for out-of-distribution CO$_2$RR on Cu, comparable to an FS model trained on in-distribution C--C bond-breaking reactions. Finally, a large MLIP fine-tuned on 49,860 configurations gives the best overall performance across metallic and metal-oxide catalysts and was used to screen a large left-out set of bimetallic alloys, achieving a 0.15 eV MAE for $E_{r}$, even for adsorbates on unseen Miller-index surfaces such as (532). This work identifies the training configurations needed for accurate FS and FT MLIPs for catalytic reaction modeling.