Jablonka Kevin Maik, Ai Qianxiang, Al-Feghali Alexander, Badhwar Shruti, Bocarsly Joshua D, Bran Andres M, Bringuier Stefan, Brinson L Catherine, Choudhary Kamal, Circi Defne, Cox Sam, de Jong Wibe A, Evans Matthew L, Gastellu Nicolas, Genzling Jerome, Gil María Victoria, Gupta Ankur K, Hong Zhi, Imran Alishba, Kruschwitz Sabine, Labarre Anne, Lála Jakub, Liu Tao, Ma Steven, Majumdar Sauradeep, Merz Garrett W, Moitessier Nicolas, Moubarak Elias, Mouriño Beatriz, Pelkie Brenden, Pieler Michael, Ramos Mayk Caldas, Ranković Bojana, Rodriques Samuel G, Sanders Jacob N, Schwaller Philippe, Schwarting Marcus, Shi Jiale, Smit Berend, Smith Ben E, Van Herck Joren, Völker Christoph, Ward Logan, Warren Sean, Weiser Benjamin, Zhang Sylvester, Zhang Xiaoqi, Zia Ghezal Ahmad, Scourtas Aristana, Schmidt K J, Foster Ian, White Andrew D, Blaiszik Ben
Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais Switzerland
Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA.
Digit Discov. 2023 Aug 8;2(5):1233-1250. doi: 10.1039/d3dd00113j. eCollection 2023 Oct 9.
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
诸如GPT-4这样的大语言模型引起了许多科学家的兴趣。最近的研究表明,这些模型在化学和材料科学中可能会有用。为了探索这些可能性,我们组织了一场黑客马拉松。本文记录了作为此次黑客马拉松一部分而构建的项目。参与者将大语言模型用于各种应用,包括预测分子和材料的性质、设计工具的新颖界面、从非结构化数据中提取知识以及开发新的教育应用。多样的主题以及能够在不到两天的时间内生成可运行的原型这一事实凸显出,大语言模型将对我们这些领域的未来产生深远影响。丰富的创意和项目集合还表明,大语言模型的应用不仅限于材料科学和化学,还能为广泛的科学学科带来潜在益处。