Heuristic development in the use of large language models for materials science
In the ever-expanding field of Artificial Intelligence (AI) tools, Generative Pre-trained Transformer (GPT) is one of many Large Language Models (LLMs) that has revolutionized how academia can interact with data. Simple sentences can be fed into GPTs to return long paragraphs of explanation, informa...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/176081 |
| _version_ | 1811682399332859904 |
|---|---|
| author | Chye, Vincent Zhen Guang |
| author2 | Leonard Ng Wei Tat |
| author_facet | Leonard Ng Wei Tat Chye, Vincent Zhen Guang |
| author_sort | Chye, Vincent Zhen Guang |
| collection | NTU |
| description | In the ever-expanding field of Artificial Intelligence (AI) tools, Generative Pre-trained Transformer (GPT) is one of many Large Language Models (LLMs) that has revolutionized how academia can interact with data. Simple sentences can be fed into GPTs to return long paragraphs of explanation, informative search results and even form discussions. This brought a spike in how information is utilised and shifted the entire tech sector in ways comparable to the birth of the internet. This feasibility study, we will harness GPT’s learning capabilities in the form of a chatbot that can bridge knowledge gaps within the field of Material Science. The method proposed is to enrich GPTs with context, through papers selected by a set of specialised search algorithm. By distilling essential research papers and textbook resources, the chatbot is tailored to generate detailed conversations within the user’s specified domain. The experimentations have used AI tools from the following: open-source models from Hugging Face, free literature database tool from Litmaps and paid model of GPT-4 by OpenAI. |
| first_indexed | 2024-10-01T03:56:13Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/176081 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:56:13Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1760812024-05-18T16:46:25Z Heuristic development in the use of large language models for materials science Chye, Vincent Zhen Guang Leonard Ng Wei Tat School of Materials Science and Engineering Maung Thway leonard.ngwt@ntu.edu.sg, maung.thway@ntu.edu.sg Computer and Information Science Engineering Large language models Domain specific transformer Chatbot In the ever-expanding field of Artificial Intelligence (AI) tools, Generative Pre-trained Transformer (GPT) is one of many Large Language Models (LLMs) that has revolutionized how academia can interact with data. Simple sentences can be fed into GPTs to return long paragraphs of explanation, informative search results and even form discussions. This brought a spike in how information is utilised and shifted the entire tech sector in ways comparable to the birth of the internet. This feasibility study, we will harness GPT’s learning capabilities in the form of a chatbot that can bridge knowledge gaps within the field of Material Science. The method proposed is to enrich GPTs with context, through papers selected by a set of specialised search algorithm. By distilling essential research papers and textbook resources, the chatbot is tailored to generate detailed conversations within the user’s specified domain. The experimentations have used AI tools from the following: open-source models from Hugging Face, free literature database tool from Litmaps and paid model of GPT-4 by OpenAI. Bachelor's degree 2024-05-13T11:38:01Z 2024-05-13T11:38:01Z 2024 Final Year Project (FYP) Chye, V. Z. G. (2024). Heuristic development in the use of large language models for materials science. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176081 https://hdl.handle.net/10356/176081 en application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Engineering Large language models Domain specific transformer Chatbot Chye, Vincent Zhen Guang Heuristic development in the use of large language models for materials science |
| title | Heuristic development in the use of large language models for materials science |
| title_full | Heuristic development in the use of large language models for materials science |
| title_fullStr | Heuristic development in the use of large language models for materials science |
| title_full_unstemmed | Heuristic development in the use of large language models for materials science |
| title_short | Heuristic development in the use of large language models for materials science |
| title_sort | heuristic development in the use of large language models for materials science |
| topic | Computer and Information Science Engineering Large language models Domain specific transformer Chatbot |
| url | https://hdl.handle.net/10356/176081 |
| work_keys_str_mv | AT chyevincentzhenguang heuristicdevelopmentintheuseoflargelanguagemodelsformaterialsscience |