MatExpert: Decomposing Materials Discovery by Mimicking Human Experts

TL;DR

MatExpert is a novel framework that mimics human experts by combining LLMs and contrastive learning to accelerate solid-state material discovery through retrieval, transition, and generation stages.

Contribution

It introduces a new three-stage approach inspired by human workflows, integrating LLMs and contrastive learning for improved material design and discovery.

Findings

Outperforms state-of-the-art methods in material generation tasks

Achieves higher validity, distribution, and stability in generated materials

Demonstrates effectiveness across various experimental metrics

Abstract

Material discovery is a critical research area with profound implications for various industries. In this work, we introduce MatExpert, a novel framework that leverages Large Language Models (LLMs) and contrastive learning to accelerate the discovery and design of new solid-state materials. Inspired by the workflow of human materials design experts, our approach integrates three key stages: retrieval, transition, and generation. First, in the retrieval stage, MatExpert identifies an existing material that closely matches the desired criteria. Second, in the transition stage, MatExpert outlines the necessary modifications to transform this material formulation to meet specific requirements outlined by the initial user query. Third, in the generation state, MatExpert performs detailed computations and structural generation to create new materials based on the provided information. Our experimental results demonstrate that MatExpert outperforms state-of-the-art methods in material generation tasks, achieving superior performance across various metrics including validity, distribution, and stability. As such, MatExpert represents a meaningful advancement in computational material discovery using langauge-based generative models.