QE-Catalytic: Advancing Catalyst Design with a Multimodal AI Model

This research explores the application of a graph-language multimodal model to predict relaxed-energy in catalytic adsorption, a critical area for improving catalyst design. The paper's contribution lies in the novel approach to model energy prediction, using advanced AI techniques.

• •Presents a novel multimodal AI model for predicting relaxed-energy in catalytic adsorption.
• •Utilizes a graph-language approach, suggesting integration of structural and textual data.
• •Potentially significant for accelerating catalyst discovery and optimization.