000145376 001__ 145376
000145376 005__ 20241024135331.0
000145376 0247_ $$2doi$$a10.3390/signals5020010
000145376 0248_ $$2sideral$$a140119
000145376 037__ $$aART-2024-140119
000145376 041__ $$aeng
000145376 100__ $$ade Curtò, J.
000145376 245__ $$aLarge Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis
000145376 260__ $$c2024
000145376 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145376 5203_ $$aX-ray photoelectron spectroscopy (XPS) remains a fundamental technique in materials science, offering invaluable insights into the chemical states and electronic structure of a material. However, the interpretation of XPS spectra can be complex, requiring deep expertise and often sophisticated curve-fitting methods. In this study, we present a novel approach to the analysis of XPS data, integrating the utilization of large language models (LLMs), specifically OpenAI’s GPT-3.5/4 Turbo to provide insightful guidance during the data analysis process. Working in the framework of the CIRCE-NAPP beamline at the CELLS ALBA Synchrotron facility where data are obtained using ambient pressure X-ray photoelectron spectroscopy (APXPS), we implement robust curve-fitting techniques on APXPS spectra, highlighting complex cases including overlapping peaks, diverse chemical states, and noise presence. Post curve fitting, we engage the LLM to facilitate the interpretation of the fitted parameters, leaning on its extensive training data to simulate an interaction corresponding to expert consultation. The manuscript presents also a real use case utilizing GPT-4 and Meta’s LLaMA-2 and describes the integration of the functionality into the TANGO control system. Our methodology not only offers a fresh perspective on XPS data analysis, but also introduces a new dimension of artificial intelligence (AI) integration into scientific research. It showcases the power of LLMs in enhancing the interpretative process, particularly in scenarios wherein expert knowledge may not be immediately available. Despite the inherent limitations of LLMs, their potential in the realm of materials science research is promising, opening doors to a future wherein AI assists in the transformation of raw data into meaningful scientific knowledge.
000145376 536__ $$9info:eu-repo/grantAgreement/ES/MCIN/AEI/PID2021-122580NB-I00
000145376 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145376 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145376 700__ $$0(orcid)0000-0002-5844-7871$$ade Zarzà, I.$$uUniversidad de Zaragoza
000145376 700__ $$aRoig, Gemma
000145376 700__ $$aCalafate, Carlos T.
000145376 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000145376 773__ $$g5, 2 (2024), 181-201$$tSignals (Basel)$$x2624-6120
000145376 8564_ $$s508522$$uhttps://zaguan.unizar.es/record/145376/files/texto_completo.pdf$$yVersión publicada
000145376 8564_ $$s2661525$$uhttps://zaguan.unizar.es/record/145376/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145376 909CO $$ooai:zaguan.unizar.es:145376$$particulos$$pdriver
000145376 951__ $$a2024-10-24-12:11:35
000145376 980__ $$aARTICLE