Data-Efficient Machine Learning Potentials for Organic Reactions through Active-Learning-Guided Fine-Tuning
Year: 2026
Type: preprint
Abstract: Transition states (TSs) are central to reaction barriers and mechanisms, yet their accurate localization remains computationally demanding. Reactive machine learning potentials can accelerate TS optimization by rapidly predicting potential energy surfaces, but achieving chemical transferability requires extensive sampling and quantum chemical (QC) labeling of reactive conformations. Here, we introduce an active-learning-guided fine-tuning strategy that couples pretrained chemical knowledge for broad generalization with adaptive labeling of informative reactive conformations for data efficiency. The resulting MACEPre-DeePEST-OS enables efficient TS optimization and barrier predictions across diverse organic reactions while requiring QC labeling for only ~2% of approximately 19 million candidate conformations. Across three in-distribution benchmarks, MACEPre-DeePEST-OS achieves an average root mean square deviation of 0.080 Å for TS geometries and a mean absolute error (MAE) of 0.938 kcal/mol for barriers. In cross-dataset evaluation on an external OrgReact subset, fine-tuning reduces the barrier MAE by 58% relative to the pretrained model. Out-of-distribution testing on a Ugi reaction network containing structures with more than 70 atoms yields a barrier MAE of 1.09 kcal/mol, indicating transfer to molecular sizes and reaction complexity beyond the fine-tuning data. These results establish a data-efficient route toward transferable reactive potentials for quantitative analysis of reaction mechanisms and competing pathways.
Source: ChemRxiv
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214085804