Active Machine Learning for Combinatorial Exploration of Metal-Insulator Transitions

We apply a combination of unsupervised and active machine learning algorithms to dynamically map structural phases associated with metal-insulator transitions using sparse X-ray diffraction (XRD) measurements of composition spread thin film libraries. As each XRD pattern is collected, we jointly perform matrix factorization and clustering to identify distinct phases. Semi-supervised learning algorithms extend the clustering results to obtain a probabilistic prediction of the phase diagram, to which we apply an active learning criterion to select a sequence of measurements to maximally increase the confidence of the model for the predicted phase diagram. We demonstrate that our method is nearly an order of magnitude more efficient than dense measurements for rapidly identifying the metal-insulator transition in the VO₂-NbO₂ system, even when restricting the sampling order to monotonic increases in temperature. Active sampling promises to enable efficient exploration of chemical systems with more than three components with adaptive resolution in composition and temperature. Future work will focus on expanding to other experimental workflows and integrating the synthesis and measurement steps into online experimental systems for optimizing functional properties of materials.