Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents

TL;DR

This paper investigates how Large Language Models can generate hypotheses for materials discovery by using a curated dataset, a goal-driven approach, and a novel evaluation metric to accelerate the development of new materials.

Contribution

The paper introduces a new dataset, a hypothesis generation method using LLMs, and a scalable evaluation framework tailored for materials science applications.

Findings

LLM-based agents can generate relevant hypotheses for materials design.

The proposed evaluation metric effectively assesses hypothesis quality.

The dataset enables benchmarking of hypothesis generation methods in materials science.

Abstract

Materials discovery and design are essential for advancing technology across various industries by enabling the development of application-specific materials. Recent research has leveraged Large Language Models (LLMs) to accelerate this process. We explore the potential of LLMs to generate viable hypotheses that, once validated, can expedite materials discovery. Collaborating with materials science experts, we curated a novel dataset from recent journal publications, featuring real-world goals, constraints, and methods for designing real-world applications. Using this dataset, we test LLM-based agents that generate hypotheses for achieving given goals under specific constraints. To assess the relevance and quality of these hypotheses, we propose a novel scalable evaluation metric that emulates the process a materials scientist would use to evaluate a hypothesis critically. Our curated dataset, proposed method, and evaluation framework aim to advance future research in accelerating materials discovery and design with LLMs.