Learning Transition-Metal Complexes Reaction Barriers with Active Learning and Machine Learning Interatomic Potential

Fine-tuning a machine learning interatomic potential (MLIP) to accurately model a chemical reaction needs training data along the reaction path. However, that path is exactly what is unknown beforehand. Here we demonstrate a method to efficiently build such a dataset from the only knowledge of the reactant and product. We show that fine-tuning only works when this method is robust. We use a combination of active learning and a custom path generator. This generator is named curved reactive scan, in which each reactive bond follows its own power law, feeding a certified saddle search whose by-products constitute the training set. As a result these data points sit on the reaction path. We show that the fine-tuning is data efficient: for 25 transition-metal complex reactions from the benchmark MOBH35 (15–67 atoms), we generate 86 to 284 density functional theory (ωB97M-V/def2- TZVPD) labels per reaction, and the fine-tuned versions of UMA S 1.2 improve the retrieving of their transition states, forward and reverse energy barriers, at chemical accuracy on average. Those results outperform the pretrained model, even though it was trained on OMol25, a data set that contains reactive configurations generated from these very MOBH35 systems. Our work shows that blind generation of molecular geometries along the reaction path is a crucial step towards efficient MLIP fine-tuning, to potentially unlock applications in metalloprotein studies, and transition metal catalyst discovery.