Adsorption Energy

Adsorption energy, the energy change upon binding of a molecule to a surface, is crucial for understanding and designing catalysts and separation materials. Current research heavily utilizes machine learning, particularly graph neural networks and deep reinforcement learning, to predict adsorption energies efficiently, often leveraging large datasets like the Open Catalyst datasets. These models are being refined to improve accuracy, explainability, and the ability to handle complex mixtures and diverse material classes, accelerating materials discovery and design for applications such as carbon capture and catalysis. The development of robust and reliable predictive models is significantly impacting the speed and efficiency of materials research.