AIMNet2-rxn: A Machine Learned Potential for Generalized Reaction Modeling on a Millions-of-Pathways Scale

Mechanistic modeling of chemical transformations offers a compelling basis for understanding reactivity and allows for prediction of reaction outcomes before attempting experiments. Despite progress in machine learned interatomic potentials (MLIPs), we demonstrate that available models lack the accuracy for diverse reaction modeling. With this motivation, we developed a general MLIP for mechanistic modeling of organics, AIMNet2-rxn, using a dataset of ~4.7 x 106 range-separated DFT calculations. AIMNet2-rxn enables reaction modeling ~106 faster than the reference quantum mechanical (QM) methods while significantly outperforming graph-based ML, reaffirming the value using 3D chemical information for training. On a test suite of well-known reaction mechanisms—such as amide formation, proton transfers, and pericyclics—AIMNet2-rxn yields 1-2 kcal mol-1 accuracy across reaction coordinates without retraining or system-specific fine-tuning. To exploit GPU parallelism and AIMNet2-rxn efficiency, we introduce a batched nudged elastic band (BNEB) method that readily achieves minimum energy pathway search on a millions-of-reactions scale. To demonstrate complex reaction characterization, the thermodynamics of an 11-step pathway producing hydroxymethylfurfural, the experimentally observed major product of glucose pyrolysis, is evaluated. Overall, the accuracy and efficiency afforded by AIMNet2-rxn creates opportunities in high-throughput reaction discovery and deep reaction network analysis that would be infeasible with QM methods.