Accelerating Material Discovery for Metal Organic Frameworks using Large Language Models

Sultan Alrowili, Mathan Kumar Eswaran, Shantanu Godbole

Keywords: MOF, CO2 Capture, Material Discovery, Metal Organic Frameworks, SMILES, LLM

TL;DR: Building small LLMs to address Metal Organic Frameworks (MOF) tasks

Abstract: Recent advancements in Machine Learning (ML) have substantially accelerated the material discovery field, yet the utilization of Large Language Models (LLMs) in the Metal-Organic Frameworks (MOFs) research has received limited attention. This work leverages LLMs to build a new set of models that accelerate MOF material discovery. Our strategy relies on pre-training the Granite model using a single H100 GPU on a combination of selective chemical journals and structural data from the PubChem database. Our evaluation demonstrates that this pre-training strategy significantly enhances the performance of LLMs in predicting MOF properties, especially in limited-resource task scenarios. We hope this work can motivate future research to explore the potential of LLMs in enhancing material discovery to build robust and efficient Metal-Organic Frameworks models.

Submission Track: Paper Track (Short Paper)

Submission Category: AI-Guided Design

Institution Location: Riyadh, Saudi Arabia

Submission Number: 64