Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned-Reference-Cited by-同舟云学术

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Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned

Published:2022 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models
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Author:

Horawalavithana Sameera,Ayton Ellyn,Sharma Shivam,Howland Scott,Subramanian Megha,Vasquez Scott,Cosbey Robin,Glenski Maria,Volkova Svitlana

Publisher

Association for Computational Linguistics

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. How Well Do Large Language Models Understand Tables in Materials Science?;Integrating Materials and Manufacturing Innovation;2024-07-19

2. Bidirectional generation of structure and properties through a single molecular foundation model;Nature Communications;2024-03-14

3. Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units;Journal of Chemical Information and Modeling;2024-02-21

4. FORGE: Pre-Training Open Foundation Models for Science;Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis;2023-11-11

5. Evaluation of pre-training large language models on leadership-class supercomputers;The Journal of Supercomputing;2023-06-16

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