# Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis
## 1 Introduction and Overview  
X-ray photoelectron spectroscopy (XPS) [1] is a powerful tool extensively used in materials science and surface chemistry. It is renowned for its capacity to provide information about the elemental composition, chemical state, and electronic structure of materials. However, the interpretation of XPS data can be a challenging task, as it often involves addressing overlapping peaks, diverse chemical states, and noise present in the spectra [2]. Traditionally, the analysis of XPS data has required significant expertise, with researchers relying on knowledge of chemical shifts, energy levels, and peak characteristics. While various software tools are available to aid in the curve-fitting process, e.g., CasaXPS (Casa Software Ltd., Teignmouth, Devon, UK), KolXPD (Kolibrik_net, s.r.o., Zd'ar nad Sazavou, Czech Republic), PeakFit (Systat Software Inc., San Jose, CA, USA), or Igor Pro (Wavemetrics, Lake Oswego, OR, USA), their efficacy is still heavily dependent on the user's expert knowledge and judgment. In recent years, advancements in artificial intelligence (AI) have prompted a shift in how we approach complex data interpretation tasks. Large language models (LLMs) [3; 4; 5; 6], trained on diverse datasets, have demonstrated an impressive ability to generate human-like text, answer questions, and even provide insights on complex problems. These models mainly build on the Transformer architecture [7; 8; 9] and advanced training mechanisms to accomplish convergence with billions of hyperparameters [10]. This study explores the utilization of OpenAI's Generative Pre-trained Transformer (GPT), in particular GPT-3.5 Turbo [11; 12], an advanced LLM, to facilitate the analysis of XPS data. The objective of this study is two-fold. First, we aim to enhance the curve-fitting process of XPS data obtained from the CIRCE-NAPP beamline at the CELLS ALBA Synchrotron facility in Cerdanyola del Valles, addressing the challenges posed by overlapping peaks, diverse chemical states, and noise. Second, we seek to incorporate the LLM into the data interpretation process, thereby aiding researchers in extracting meaningful insights from the fitted parameters. The goal is to improve human expertise, enabling a more streamlined and efficient process for data analysis. This paper further explores the strengths and limitations of using an LLM for such a purpose, and highlights the potential of AI in transforming the way we conduct and interpret scientific research. We also describe the integration of this functionality within the TANGO control system used at the CELLS ALBA Synchrotron. By integrating AI into the workflow, we open up new avenues for accelerated scientific discovery, and bring a fresh perspective to the existing methodologies of data interpretation in material science research. This study also highlights the value of ongoing collaboration between AI development and the scientific community to ensure the optimal utilization of these advanced models. We envision a future wherein AI, particularly LLMs, play a significant role in the analysis and interpretation of scientific data, augmenting human expertise and enabling more researchers to leverage the power of techniques like XPS. As we continue to explore the interface between AI and scientific research, it becomes increasingly evident that the marriage of these two fields holds considerable promise. The remainder of this paper is structured as follows: Section 2 delves into the relevant literature and prior studies, presenting a comprehensive review of the works that have contributed to the current state of knowledge in the field. In Section 3, we provide an overview of XPS, delineating its core principles, instrumentation, and applications. This is further refined in Section 3.1, where APXPS, a specific variant of XPS, is elucidated, emphasizing its unique capabilities and significance. Section 4 articulates our research methodology, giving emphasis to the materials, techniques, and procedures adopted throughout the study.