Exploring transferability of machine learning interatomic potentials for reactive chemistry

The development of machine learning interatomic potentials (MLIP) has resulted in the ability to model high-quality potential energy surfaces with near ab initio level of accuracy at low computational cost. However, just like other machine learning models, MLIP faces challenges when it comes to transferability, specifically to systems of chemical space beyond its training. Here we explore sampling techniques that can allow MLIP such as ANI and NequIP to obtain transferability beyond its training data to achieve accurate bond dissociation across chemical space.