A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916487499874304 |
|---|---|
| author | Liu, Siyu Wen, Tongqi Pattamatta, A. S. L. Subrahmanyam Srolovitz, David J. |
| author_facet | Liu, Siyu Wen, Tongqi Pattamatta, A. S. L. Subrahmanyam Srolovitz, David J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17788 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification Liu, Siyu Wen, Tongqi Pattamatta, A. S. L. Subrahmanyam Srolovitz, David J. Materials Science 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. |
| title | A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification |
| topic | Materials Science |
| url | https://arxiv.org/abs/2401.17788 |