M. Martyka

Active delta-learning for surface hopping: efficient fitting of potential energy surfaces.

This work combines active learning with Δ-learning - where the ML model predicts the difference between a target quantum chemical method and a computationally efficient baseline - into an end-to-end protocol for active delta-learning (ADL) of potential energy surfaces for trajectory surface hopping dynamics.