Large Language Model-Guided Prediction Toward Quantum Materials Synthesis

TL;DR

This paper introduces a large language model-based framework to predict synthesis pathways for inorganic and quantum materials, significantly improving accuracy over previous models and aiding quantum materials discovery.

Contribution

The authors develop a novel LLM-based framework with three specialized models for predicting synthesis reactions, achieving high accuracy and robustness in quantum materials synthesis prediction.

Findings

Model accuracy improved from under 40% to around 90%.

Framework maintains robustness with additional synthesis steps.

Comparable performance across materials with different quantum weights.

Abstract

The synthesis of inorganic crystalline materials is essential for modern technology, especially in quantum materials development. However, designing efficient synthesis workflows remains a significant challenge due to the precise experimental conditions and extensive trial and error. Here, we present a framework using large language models (LLMs) to predict synthesis pathways for inorganic materials, including quantum materials. Our framework contains three models: LHS2RHS, predicting products from reactants; RHS2LHS, predicting reactants from products; and TGT2CEQ, generating full chemical equations for target compounds. Fine-tuned on a text-mined synthesis database, our model raises accuracy from under 40% with pretrained models, to under 80% using conventional fine-tuning, and further to around 90% with our proposed generalized Tanimoto similarity, while maintaining robust to additional synthesis steps. Our model further demonstrates comparable performance across materials with varying degrees of quantumness quantified using quantum weight, indicating that LLMs offer a powerful tool to predict balanced chemical equations for quantum materials discovery.