Why It's Important: Rapidly and efficiently identifying new materials with desired physical and functional properties is a longstanding challenge with significant scientific and economic implications. Evaluating vast spaces of possible materials can be an intractable problem both experimentally in the lab, and computationally via ab-initio simulations. Generative AI and Large Language Models (LLMs) provide a promising avenue to address these current challenges in materials discovery by significantly improving the efficiency and success rate in materials discovery. In particular, LLMs have shown exceptional capability for reasoning, self-reflection, and decision-making in a range of different domains, including in the physical sciences.
Our Approach: We seek to leverage these capabilities of LLMs, namely, 1) their ability to interpret human instructions and guidance, 2) their ability to reason and make decisions, and 3) their ability to internalize scientific knowledge, to create an agent which can intelligently design new materials with human-like capabilities. In this project we will develop and systematically benchmark, in silico, an LLM-driven framework for automated computational and experimental materials design with natural language-guided behavior and advanced reasoning capabilities. This approach represents a paradigm shift from existing materials design approaches based on high-throughput screening or traditional optimization methods using Bayesian methods or genetic algorithms, with the potential to enable and accelerate the design of novel materials with transformative properties.