MatExpert: Decomposing Materials Discovery by Mimicking Human Experts

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 stateof-the-art methods in material generation tasks, achieving superior performance across various metrics including validity, distribution, and stability. As such, Mat-Expert represents a meaningful advancement in computational material discovery using langauge-based generative models. The discovery and design of new materials are central challenges in modern materials science, driven by the need for materials with tailored properties for applications in energy, electronics, and catalysis. Traditional methods for material discovery, such as high-throughput experiments and density functional theory (DFT) simulations, are computationally expensive and often require significant domain expertise to achieve accurate predictions (Miret et al., 2024). Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), have opened new possibilities for automating and accelerating the materials design process (Miret & Krishnan, 2024; Jablonka et al., 2024; Song et al., 2023a;b; Zhang et al., 2024; Ramos et al., 2024). LLMs such as GPT-4 OpenAI (2023) have demonstrated remarkable success in natural language processing tasks and have shown potential for application in scientific problems beyond language, including chemistry and materials science Flam-Shepherd & Aspuru-Guzik (2023); Gruver et al. (2024); Schilling-Wilhelmi et al. (2024); Mirza et al. (2024); Delétang et al. (2023). For example, LLMs have been used to generate molecular structures Gruver et al. (2024) and predict material properties from textual descriptions Alampara et al. (2024).