LLM-Driven Synthesis Planning for Quantum Dot Materials Development.

Abstract

The application of large language models in materials science has opened new avenues for accelerating materials development. Building on this advancement, we propose a novel framework leveraging large language models to optimize experimental procedures for synthesizing quantum dot materials with multiple desired properties. Our framework integrates the synthesis protocol generation model and the property prediction model, both fine-tuned on open-source large language models using parameter-efficient training techniques with in-house synthesis protocol data. Once the synthesis protocol with target properties and a masked reference protocol is generated, it undergoes validation through the property prediction models, followed by assessments of its novelty and human evaluation. Our synthesis experiments demonstrate that among the six synthesis protocols derived from the entire framework, three successfully update the Pareto front, and all six improve at least one property. Through empirical validation, we confirm the effectiveness of our fine-tuned large language model-driven framework for synthesis planning, showcasing strong performance under multitarget optimization.