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Condensed Matter > Materials Science
Title: A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification
(Submitted on 31 Jan 2024 (v1), last revised 27 Mar 2024 (this version, v2))
Abstract: Large language models (LLMs) have demonstrated rapid progress across a wide array of domains. Owing to the very large number of parameters and training data in LLMs, these models inherently encompass an expansive and comprehensive materials knowledge database, far exceeding the capabilities of individual researcher. Nonetheless, devising methods to harness the knowledge embedded within LLMs for the design and discovery of novel materials remains a formidable challenge. We introduce a general approach for addressing materials classification problems, which incorporates LLMs, prompt engineering, and deep learning. Utilizing a dataset of metallic glasses as a case study, our methodology achieved an improvement of up to 463% in prediction accuracy compared to conventional classification models. These findings underscore the potential of leveraging textual knowledge generated by LLMs for materials especially in the common situation where datasets are sparse, thereby promoting innovation in materials discovery and design.
Submission history
From: Tongqi Wen [view email][v1] Wed, 31 Jan 2024 12:31:52 GMT (22059kb,D)
[v2] Wed, 27 Mar 2024 13:22:22 GMT (22059kb,D)
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