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).

Stable states in complex systems correspond to local minima on the associated potential energy surface. Transitions between these local minima govern the dynamics of such systems. Precisely determining the transition pathways in complex and high-dimensional systems is challenging because these transitions are rare events, and isolating the relevant species in experiments is difficult. Most of the time, the system remains near a local minimum, with rare, large fluctuations leading to transitions between minima. The probability of such transitions decreases exponentially with the height of the energy barrier, making the system's dynamics highly sensitive to the calculated energy barriers. This work aims to formulate the problem of finding the minimum energy barrier between two stable states in the system's state space as a cost-minimization problem. We propose solving this problem using reinforcement learning algorithms. The exploratory nature of reinforcement learning agents enables efficient sampling and determination of the minimum energy barrier for transitions.

Understanding the transition events between metastable states in complex systems is an important subject in the fields of computational physics, chemistry and biology. The transition pathway plays an important role in characterizing the mechanism underlying the transition, for example, in the study of conformational changes of bio-molecules. In fact, computing the transition pathway is a challenging task for complex and high-dimensional systems. In this work, we formulate the path-finding task as a cost minimization problem over a particular path space. The cost function is adapted from the Freidlin-Wentzell action functional so that it is able to deal with rough potential landscapes. The path-finding problem is then solved using a actor-critic method based on the deep deterministic policy gradient algorithm (DDPG). The method incorporates the potential force of the system in the policy for generating episodes and combines physical properties of the system with the learning process for molecular systems. The exploitation and exploration nature of reinforcement learning enables the method to efficiently sample the transition events and compute the globally optimal transition pathway. We illustrate the effectiveness of the proposed method using three benchmark systems including an extended Mueller system and the Lennard-Jones system of seven particles.