Walter Malone

Active Learning for Optimizing Adsorption Energy Predictions in Large Chemical Spaces.

A global active learning framework is demonstrated to map these landscapes efficiently by coupling genetic algorithms with deep neural networks trained on density functional theory data, which provides a scalable and resource-efficient strategy for high-throughput materials discovery in applications such as hydrogen storage and ammonia synthesis.