Fig. 2: Flow chart of UQ-driven active learning framework.
From: Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning
The UQ-AL framework is composed of two computational platforms, the high-throughput microscopic MD simulations, and PIML-UQ platform. The UQ-AL workflow begins by generating and learning from an initial dataset, and then progressively queries new datasets where the trained models exhibit the most uncertainty. In each iteration, we independently train an ensemble of identical GNN models with randomized initialization of weights and biases. These models are then evaluated on newly generated datasets with randomly sampled simulation parameters, and the uncertainties of the models' single-step predictions are quantified. We integrate the top 50% of new configurations that induce the highest predictive uncertainty into the existing training dataset. The PI-GNN ensemble is retrained on this augmented dataset, and the process repeats.
