Electronic Stopping Power Predictions from Machine Learning

We aim to develop an affordable computational approach that provides the electronic stopping power for arbitrary trajectories of ions impacting a target material with an accuracy comparable to that of modern quantum mechanical first-principles simulations. Currently, real-time time-dependent density functional theory can accomplish this in reasonable agreement with experiment. However, the computational cost of this method is high which limits the number of trajectories and host material atomic geometries that can be studied. This prevents a routine integration of electronic-stopping power, e.g. in the molecular dynamics simulation of radiation damage cascades. We use cutting-edge descriptors of atomic geometries to train modern machine-learning models on data from real-time time-dependent density functional theory. We find very low error bars and very high accuracy at million-fold reduced computational cost of the trained model for proton irradiated aluminum. We also are able to predict velocity dependent electronic stopping and entire Bragg peak simulations with our models. In this presentation we discuss our framework in detail as well as its broad applicability in the particle-radiation community, including target materials with complex atomic geometry or low-dimensional materials.