Assessment of Fine-Tuned Large Language Models for Real-World Chemistry and Material Science Applications Article Swipe

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Joren Van Herck , M.V. Gil , Kevin Maik Jablonka , Alex Abrudan , Andy S. Anker , Mehrdad Asgari , Ben Blaiszik , Leander Choudhury , Clémence Corminbœuf , Hilal Daglar , Ian Foster , Susana García , Matthew Garvin , Guillaume Godin , Lydia L. Good , Jianan Gu , N.‐X. Hu , Xin Jin , Tanja Junkers , Seda Keskın , Tuomas P. J. Knowles , Rubén Laplaza , Sauradeep Majumdar , Hossein Mashhadimoslem , Ruaraidh D. McIntosh , Seyed Mohamad Moosavi , Beatriz Mouriño , Francesca Nerli , C. Pevida , Neda Poudineh , Mahyar Rajabi-Kochi , Kadi L. Saar , Fahimeh Hooriabad Saboor , Morteza Sagharichiha , K. J. Schmidt , Jiale Shi , Dennis Svatunek , Marco Taddei , Igor V. Tetko , D. Tolnai , Sahar Vahdatifar , Jonathan K. Whitmer , D.C. Florian Wieland , Regine Willumeit‐Römer , Andreas Züttel , Berend Smit ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.26434/chemrxiv-2024-mm31v

The current generation of large language models (LLMs), like ChatGPT, have limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. In this work, we explore the potential and limitations of this approach. We studied the performance of fine-tuning GPT-J-6B, a public-domain version of the GPT family, for a range of different chemical questions. We find that in most, if not all, cases, this approach outperforms the benchmark (random guessing) for a simple classification problem. Depending on the size of the dataset and the type of questions, we can also address more sophisticated problems. The most important conclusions of this work are that, for all datasets considered, their conversion into an LLM fine-tuning training set is straightforward and that fine-tuning with even relatively small datasets leads to predictive models. These results suggest that the systematic use of LLMs to guide experiments and simulations will be a powerful technique in any research study, significantly reducing unnecessary experiments or computations.

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Artificial Intelligence

Mechanical Engineering

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Programming Language

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Benchmark (surveying) Set (abstract data type) Computer science Range (aeronautics) Computation Work (physics) Simple (philosophy) Fine-tuning Machine learning Artificial intelligence Algorithm Materials science Epistemology Engineering Geography Geodesy Philosophy Quantum mechanics Composite material Mechanical engineering Physics Programming language

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Type: preprint
Language: en
Landing Page: https://doi.org/10.26434/chemrxiv-2024-mm31v
PDF: https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/668bd7385101a2ffa8c1e559/original/assessment-of-fine-tuned-large-language-models-for-real-world-chemistry-and-material-science-applications.pdf
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Cited By: 2
References: 34
Related Works: 10
OpenAlex ID: https://openalex.org/W4401183144

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DOI: https://doi.org/10.26434/chemrxiv-2024-mm31v

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Title: Assessment of Fine-Tuned Large Language Models for Real-World Chemistry and Material Science Applications

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-07-31

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Authors: Joren Van Herck, M.V. Gil, Kevin Maik Jablonka, Alex Abrudan, Andy S. Anker, Mehrdad Asgari, Ben Blaiszik, Leander Choudhury, Clémence Corminbœuf, Hilal Daglar, Ian Foster, Susana García, Matthew Garvin, Guillaume Godin, Lydia L. Good, Jianan Gu, N.‐X. Hu, Xin Jin, Tanja Junkers, Seda Keskın, Tuomas P. J. Knowles, Rubén Laplaza, Sauradeep Majumdar, Hossein Mashhadimoslem, Ruaraidh D. McIntosh, Seyed Mohamad Moosavi, Beatriz Mouriño, Francesca Nerli, C. Pevida, Neda Poudineh, Mahyar Rajabi-Kochi, Kadi L. Saar, Fahimeh Hooriabad Saboor, Morteza Sagharichiha, K. J. Schmidt, Jiale Shi, Dennis Svatunek, Marco Taddei, Igor V. Tetko, D. Tolnai, Sahar Vahdatifar, Jonathan K. Whitmer, D.C. Florian Wieland, Regine Willumeit‐Römer, Andreas Züttel, Berend Smit

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Landing page: https://doi.org/10.26434/chemrxiv-2024-mm31v

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Concepts: Benchmark (surveying), Set (abstract data type), Computer science, Range (aeronautics), Computation, Work (physics), Simple (philosophy), Fine-tuning, Machine learning, Artificial intelligence, Algorithm, Materials science, Epistemology, Engineering, Geography, Geodesy, Philosophy, Quantum mechanics, Composite material, Mechanical engineering, Physics, Programming language

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Cited by: 2

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