Active Learning Driven Materials Exploration With Uncertainty Quantification

via “active learning-driven materials exploration with uncertainty quantification”

* ⏫ 12/2023: [Discovery of a structural class of antibiotics with explainable deep learning](https://www.nature.com/articles/s41586-023-06887-8)

Unique: Combines graph neural network predictions with ensemble-based uncertainty quantification and multi-objective acquisition functions to balance discovery of novel stable materials against predicted performance, enabling closed-loop active learning where experimental feedback directly refines the exploration strategy

vs others: More sample-efficient than random screening or greedy exploitation because it explicitly models prediction uncertainty and prioritizes high-uncertainty, high-potential regions, reducing the number of experiments needed to find competitive materials

via “model-uncertainty-quantification”