| Abstract Scope |
Machine learning (ML) interatomic potentials have become an efficient alternative to computationally expensive quantum chemistry simulations. In the case of reactive chemistry designing high-quality training data sets is crucial to overall model accuracy. To address this challenge, we develop a general reactive ML interatomic potential through unbiased active learning with an atomic configuration sampler inspired by nanoreactor molecular dynamics. The resulting model is then applied to study five distinct condensed-phase reactive chemistry systems. The model does not need to be refit for each application, enabling high throughput in silico reactive chemistry experimentation. Active learning can be further boosted with uncertainty driven dynamics that can rapidly discover configurations tot meaningfully augment the training data set. Altogether, explosive growth of user-friendly ML frameworks, designed for chemistry, demonstrates that the field is evolving towards physics-based models augmented by data science. |