Active Learning Driven Machine Learning Inter-Atomic Potentials Generation: A Case Study for Hafnium dioxide

We propose a novel active learning scheme to automate the configuration selection to fit the Gaussian Approximation Potential (GAP). The proposed scheme consists of an unsupervised active sampler coupled to a Bayesian optimization to evaluate the GAP model. We apply this scheme to abintio molecule dynamics trajectories of Hafnium dioxide. We will show that this scheme leads to a much lower number of training configuration that arrives at near abintio energy fit accuracy as evaluated by an error metric. With the active learned GAP model, we performed molecule dynamics (MD) simulation. We show that the MD simulation calculated x-ray structural factors are in the good agreement with experiments.