# Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis
## 2 Related Works  
The application of XPS [2] in the field of materials science has a well-established history, with extensive literature documenting methodologies for data acquisition, analysis, and interpretation. However, the integration of artificial intelligence (AI) into this field is a relatively novel concept, presenting an array of potential enhancements to traditional approaches.  
In the realm of XPS data analysis, a wealth of literature focuses on the use of conventional curve-fitting techniques. Seminal works such as those in [13; 14] provide comprehensive overviews of these techniques, discussing in depth the challenges related to peak overlap, the presence of noise, and the interpretation of chemical states. These papers underscore the need for expert judgment and the importance of careful peak parameter selection, shaping much of the contemporary thinking in this field.  
Recently, more sophisticated curve-fitting methods, including iterative and machine learning (ML)-based techniques, have begun to emerge. Studies such as the one in [15] propose the use of ML algorithms for automatic peak detection, demonstrating promising results in terms of precision and computational efficiency.  
The usage of AI, and more specifically LLMs [11, 12], in the scientific field has seen a surge in recent years. OpenAI's GPT-3, the predecessor to GPT-3.5 Turbo, has been utilized in various scientific domains [16, 17, 18, 19]. These studies highlight the capability of LLMs to generate informative, contextually relevant content, and suggest the potential for their application in more specialized scientific tasks [20, 21, 22].  
The integration of AI into XPS data analysis, however, remains relatively unexplored. [PERSON] et al., 2022 [23], put forth an innovative ML model that merges density functional theory (DFT) with GW (a theory named after the physicists [PERSON]'s Green function (G) and screened Coulomb interaction (W)) [24]; the GW method is a post-Hartree-Fock [25] approach for the many-body treatment of electron-electron interactions. It is widely used for the calculation of the electronic properties of systems, particularly in the field of condensed matter physics. As already introduced, the \"G\" in GW refers to [PERSON]'s function, which describes the propagation of electrons in a material, while \"W\" denotes the screened Coulomb interaction between these electrons. This method is of utmost importance in the field of many-body physics and is widely recognized by its conventional designation \"GW\". In [26], they introduced an AI ab initio framework specifically tailored for predicting XPS data for the solid electrolyte interphase (SEI) of lithium-metal batteries. To the best of our knowledge, this study represents the first attempt to leverage an LLM like GPT-3.5 Turbo in the interpretation of XPS spectra, marking a unique contribution to both the field of materials science and AI applications in scientific research.  
It is worth noting that, while the literature provides ample evidence of the potential benefits of AI in scientific data analysis, it also underscores the challenges and limitations inherent to these methods. The need for extensive training data, the risks of over-reliance on AI interpretations, and the occasional opacity of AI decision-making processes, are just a few of the issues highlighted in previous works. As such, while this study seeks to explore the potential of LLMs in XPS data interpretation, we acknowledge and address these challenges throughout our research.