Assessment of fine-tuned large language models for real-world chemistry and material science applications

The datasets and Jupyter Notebooks used in this work are available at https://github.com/JorenBE/GPT-Challenge.

Detalles Bibliográficos
Autores:	Van Herck, Joren, Gil Matellanes, María Victoria, Jablonka, Kevin Maik, Abrudan, Alex, Anker, Andy S, Asgari, Mehrdad, Blaiszik, Ben, Buffo, Antonio, Choudhury, Leander, Corminboeuf, Clemence, Daglar, Hilal, Elahi, Amir Mohammad, Foster, Ian T, Garcia, Susana, Garvin, Matthew, Godin, Guillaume, Good, Lydia L, Gu, Jianan, Xiao Hu, Noémie, Jin, Xin, Junkers, Tanja, Keskin, Seda, Knowles, Tuomas P J, Laplaza, Ruben, Lessona, Michele, Majumdar, Sauradeep, Mashhadimoslem, Hossein, McIntosh, Ruaraidh D, Moosavi, Seyed Mohamad, Mouriño, Beatriz, Nerli, Francesca, Pevida García, Covadonga, Poudineh, Neda, Rajabi-Kochi, Mahyar, Saar, Kadi L, Hooriabad Saboor, Fahimeh, Sagharichiha, Morteza, Schmidt, K J, Shi, Jiale, Simone, Elena, Svatunek, Dennis, Taddei, Marco, Tetko, Igor, Tolnai, Domonkos, Vahdatifar, Sahar, Whitmer, Jonathan, Wieland, D C Florian, Willumeit-Römer, Regine, Züttel, Andreas, Smit, Berend
Tipo de recurso:	artículo
Estado:	Versión publicada
Fecha de publicación:	2025
País:	España
Institución:	Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:	DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:	oai:digital.csic.es:10261/380150
Acceso en línea:	http://hdl.handle.net/10261/380150 https://api.elsevier.com/content/abstract/scopus_id/85212108442
Access Level:	acceso abierto
Palabra clave:	http://metadata.un.org/sdg/9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

Descripción
Sumario:	The datasets and Jupyter Notebooks used in this work are available at https://github.com/JorenBE/GPT-Challenge.