References:

1 | Introduction to LLMs and their use in materials science

 

  1. Jablonka, K. M.; Schwaller, P.; Ortega-Guerrero, A.; Smit, B. Leveraging Large Language Models for Predictive Chemistry. Nat Mach Intell 2024, 1–9.
    https://doi.org/10.1038/s42256-023-00788-1
  2. Jablonka, K. M.; Ai, Q.; Al-Feghali, A.; Badhwar, S.; D. Bocarsly, J.; M. Bran, A.; Bringuier, S.; Catherine Brinson, L.; Choudhary, K.; Circi, D.; Cox, S.; Jong, W. A. de; L. Evans, M.; Gastellu, N.; Genzling, J.; Victoria Gil, M.; K. Gupta, A.; Hong, Z.; Imran, A.; Kruschwitz, S.; Labarre, A.; Lála, J.; Liu, T.; Ma, S.; Majumdar, S.; W. Merz, G.; Moitessier, N.; Moubarak, E.; Mouriño, B.; Pelkie, B.; Pieler, M.; Caldas Ramos, M.; Ranković, B.; G. Rodriques, S.; N. Sanders, J.; Schwaller, P.; Schwarting, M.; Shi, J.; Smit, B.; E. Smith, B.; Herck, J. V.; Völker, C.; Ward, L.; Warren, S.; Weiser, B.; Zhang, S.; Zhang, X.; Ahmad Zia, G.; Scourtas, A.; J. Schmidt, K.; Foster, I.; D. White, A.; Blaiszik, B. 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon. Digital Discovery 2023, 2 (5), 1233–1250. https://doi.org/10.1039/D3DD00113J
  3. Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; Amodei, D. Language Models Are Few-Shot Learners. arXiv July 22, 2020. http://arxiv.org/abs/2005.14165 (accessed 2022-06-22)
  4. The annotated transformer: https://nlp.seas.harvard.edu/annotated-transformer/
  5. White, A. D.; Hocky, G. M.; Gandhi, H. A.; Ansari, M.; Cox, S.; Wellawatte, G. P.; Sasmal, S.; Yang, Z.; Liu, K.; Singh, Y.; Ccoa, W. J. P. Assessment of Chemistry Knowledge in Large Language Models That Generate Code. Digital Discovery 2023, 2 (2), 368–376. https://doi.org/10.1039/D2DD00087C
  6. White, A. D. The Future of Chemistry Is Language. Nat Rev Chem 2023, 1–2. https://doi.org/10.1038/s41570-023-00502-0
  7. Hocky, G. M.; White, A. D. Natural Language Processing Models That Automate Programming Will Transform Chemistry Research and Teaching. Digital Discovery 2022, 1 (2), 79–83. https://doi.org/10.1039/D1DD00009H

 

3 | Advanced applications of LLMs

 

  1. LLM Powered autonomous agents: https://lilianweng.github.io/posts/2023-06-23-agent/
  2. Yao, S.; Zhao, J.; Yu, D.; Du, N.; Shafran, I.; Narasimhan, K.; Cao, Y. ReAct: Synergizing Reasoning and Acting in Language Models. arXiv March 9, 2023. https://doi.org/10.48550/arXiv.2210.03629
  3. Karpas, E.; Abend, O.; Belinkov, Y.; Lenz, B.; Lieber, O.; Ratner, N.; Shoham, Y.; Bata, H.; Levine, Y.; Leyton-Brown, K.; Muhlgay, D.; Rozen, N.; Schwartz, E.; Shachaf, G.; Shalev-Shwartz, S.; Shashua, A.; Tenenholtz, M. MRKL Systems: A Modular, Neuro-Symbolic Architecture That Combines Large Language Models, External Knowledge Sources and Discrete Reasoning. arXiv May 1, 2022. http://arxiv.org/abs/2205.00445(accessed 2023-04-17)
  4. Bran, A. M.; Cox, S.; White, A. D.; Schwaller, P. ChemCrow: Augmenting Large-Language Models with Chemistry Tools. arXiv April 12, 2023. http://arxiv.org/abs/2304.05376 (accessed 2023-05-22)
  5. Boiko, D. A.; MacKnight, R.; Kline, B.; Gomes, G. Autonomous Chemical Research with Large Language Models. Nature 2023, 624 (7992), 570–578. https://doi.org/10.1038/s41586-023-06792-0
  6. Bran, A. M.; Schwaller, P. Transformers and Large Language Models for Chemistry and Drug Discovery. arXiv October 9, 2023. https://doi.org/10.48550/arXiv.2310.06083

 

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