# Multimodal Language and Graph Learning of Adsorption Configuration in Catalysis

arXiv: https://arxiv.org/abs/2401.07408 · HTML: https://mlsanity.com/paper/2401.07408/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2024.

## Built on by (most cited first, 15 of 15)

- [Regression with Large Language Models for Materials and Molecular Property Prediction](https://mlsanity.com/paper/2409.06080/) (2024)
- [MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models](https://mlsanity.com/paper/2506.00198/) (2025)
- [Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent](https://mlsanity.com/paper/2410.16658/) (2024)
- [Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties](https://mlsanity.com/paper/2407.03380/) (2024)
- [Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications](https://mlsanity.com/paper/2510.21131/) (2025)
- [AlloyBERT: Alloy Property Prediction with Large Language Models](https://mlsanity.com/paper/2403.19783/) (2024)
- [AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturing](https://mlsanity.com/paper/2501.17784/) (2025)
- [Polymer-Agent: Large Language Model Agent for Polymer Design](https://mlsanity.com/paper/2601.16376/) (2026)
- [Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations](https://mlsanity.com/paper/2506.14850/) (2025)
- [QE-Catalytic: A Graph-Language Multimodal Base Model for Relaxed-Energy Prediction in Catalytic Adsorption](https://mlsanity.com/paper/2512.20084/) (2025)
- [Explainable Data-driven Modeling of Adsorption Energy in Heterogeneous Catalysis](https://mlsanity.com/paper/2405.20397/) (2024)
- [Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap Prediction](https://mlsanity.com/paper/2501.03456/) (2025)
- [Meta-Learning for Cross-Task Generalization in Protein Mutation Property Prediction](https://mlsanity.com/paper/2510.20943/) (2025)
- [Generation-Augmented Generation: A Plug-and-Play Framework for Private Knowledge Injection in Large Language Models](https://mlsanity.com/paper/2601.08209/) (2026)
- [CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials](https://mlsanity.com/paper/2605.17254/) (2026)

## Abstract (arXiv)

Adsorption energy is a reactivity descriptor that must be accurately predicted for effective machine learning (ML) application in catalyst screening. This process involves determining the lowest energy across various adsorption configurations on a catalytic surface, which can exhibit very similar energy values. While graph neural networks (GNNs) have shown great success in computing the energy of catalyst systems, they rely heavily on atomic spatial coordinates. In contrast, transformer-based language models can directly use human-readable text inputs, potentially bypassing the need for detailed atomic positions. However, these language models often struggle with accurately predicting the energy of adsorption configurations. Our study addresses this limitation by introducing a self-supervised multi-modal learning approach called graph-assisted pretraining, which connects well-established GNNs with emerging language model applications. This method reduces the MAE of energy prediction for adsorption configurations by about 10%. Furthermore, our findings demonstrate that graph-assisted pretraining enhances fine-tuning with different datasets, indicating the transferability of this approach. This method also redirects the model's attention toward adsorption configuration, rather than individual adsorbate and catalyst information, similar to common domain knowledge. Building on this, we propose using generative large language models to create text inputs for the predictive model, based solely on chemical composition and surface orientation, without relying on exact atomic positions. This demonstrates a potential use case of language models in energy prediction without geometric information.