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

Optimizing catalysis requires the efficient exploration of immense chemical spaces, particularly for high-entropy (multielement) systems where properties depend on complex variables like geometry, composition, and site-specific interactions. In this work, we demonstrate a global active learning framework to map these landscapes efficiently. By coupling genetic algorithms with deep neural networks trained on density functional theory data, our approach learns the potential energy surface while optimizing chemical structures simultaneously, bypassing costly density functional theory relaxations. We apply this framework to predict H2, N2, and NH3 adsorption energies on 10-50 atom clusters composed of Ag, Au, Cu, Ni, Pd, and Pt. The model achieves a mean absolute error of less than 0.10 eV against density functional theory validation. Results identify regions with moderate H2 physisorption (-0.1 to -0.5 eV) and other regions with strong chemisorption (-1.0 to -2.0 eV). Physisorption mainly consists of adsorption on Ag, Au, Ni, and Cu, while chemisorption involves Pd and Pt sites. High-entropy environments can provide a diverse distribution of local chemical conditions. This site variance is critical for multistep reactions where intermediate steps possess conflicting optimal binding energies that cannot be simultaneously satisfied by low-entropy surfaces, thereby offering entropy as a tunable parameter for catalyst design. This framework provides a scalable and resource-efficient strategy for high-throughput materials discovery in applications such as hydrogen storage and ammonia synthesis.