Harnessing GPT-3.5 for text parsing in solid-state synthesis – case study of ternary chalcogenides

Abstract

Optimally doped single-phase compounds are necessary to advance state-of-the-art thermoelectric devices which convert heat into electricity and vice versa, requiring solid-state synthesis of bulk materials. For data-driven approaches to learn these recipes, it requires careful data curation from large bodies of text which may not be available for some materials, as well as a refined language processing algorithm which presents a high barrier of entry. We propose applying Large Language Models (LLMs) to parse solid-state synthesis recipes, encapsulating all essential synthesis information intuitively in terms of primary and secondary heating peaks. Using a domain-expert curated dataset for a specific material (Gold Standard), we engineered a prompt set for GPT-3.5 to replicate the same dataset (Silver Standard), doing so successfully with 73% overall accuracy. We then proceed to extract and infer synthesis conditions for other ternary chalcogenides with the same prompt set. From a database of 168 research papers, we successfully parsed 61 papers which we then used to develop a classifier to predict phase purity. Our methodology demonstrates the generalizability of Large Language Models (LLMs) for text parsing, specifically for materials with sparse literature and unbalanced reporting (since usually only positive results are shown). Our work provides a roadmap for future endeavors seeking to amalgamate LLMs with materials science research, heralding a potentially transformative paradigm in the synthesis and characterization of novel materials.