Adsorbate Placement

Predicting the optimal placement of adsorbates on surfaces is crucial for designing efficient catalysts and materials. Current research focuses on developing machine learning models, including graph neural networks, Bayesian optimization, and diffusion models, to accelerate and improve the accuracy of this prediction, often surpassing traditional methods in speed and/or accuracy. These advancements leverage large datasets and sophisticated algorithms to efficiently explore the vast configuration space, enabling faster and more accurate computational catalysis. The resulting improvements have significant implications for materials discovery and the development of new technologies in areas such as clean energy.