Multimodal language and graph learning of adsorption configuration in catalysis

Adsorption energy is a reactivity descriptor that must be accurately predicted for effective machine learning application in catalyst screening. This process involves finding the lowest energy among different adsorption configurations on a catalytic surface, which often have very similar energies. Although graph neural networks have shown great success in computing the energy of catalyst systems, they rely heavily on atomic spatial coordinates. By contrast, transformer-based language models can directly use human-readable text inputs, potentially bypassing the need for detailed atomic positions or topology; however, these language models often struggle with accurately predicting the energy of adsorption configurations. Our study improves the predictive language model by aligning its latent space with well-established graph neural networks through a self-supervised process called graph-assisted pretraining. This method reduces the mean absolute error of energy prediction for adsorption configurations by 7.4–9.8%, redirecting the model’s attention towards adsorption configuration. Building on this, we propose using generative large language models to create text inputs for the predictive model without relying on exact atomic positions. This demonstrates a potential use case of language models in energy prediction without detailed geometric information. Ock and colleagues explore predictive and generative language models for improving adsorption energy prediction in catalysis without relying on exact atomic positions. The method involves aligning a language model’s latent space with graph neural networks using graph-assisted pretraining.