# Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis
## 9 Conclusions and Further Work  
This study presents a novel methodology that combines traditional curve-fitting techniques with the advanced natural language processing capabilities of a large language model (LLM) to analyze X-ray photoelectron spectroscopy (XPS) data. By utilizing synthetic data simulating complex XPS spectra, we successfully demonstrated that this hybrid approach could extract meaningful features from the data and provide expert-like interpretation to facilitate understanding for a broad range of users.  
The integration of an LLM into the data interpretation process significantly simplifies the complex task of understanding XPS data. By translating raw data features into human-understandable insights, the LLM acts as a bridge between quantitative data and qualitative understanding. It democratises the process, allowing non-experts to benefit from the insights typically reserved for domain specialists.  
However, as with any model-based system, the quality of the outputs is contingent upon the quality of the inputs. Thus, careful consideration and cross-validation with domain experts should always accompany LLM interpretation. This is especially true as we are dealing with synthetic data in this study and a particular use-case, and the translation to a broad range of real-world XPS data may reveal additional complexities.  
In terms of further work, refining this approach with more use cases derived from XPS beamline experiments stands as a natural progression. Given the complexity of real XPS data, the curve-fitting procedure may benefit from more sophisticated models capable of handling intricate overlaps, broader peaks, and high levels of noise. Additionally, the performance of the LLM could potentially be enhanced by fine-tuning it with a more specific dataset focused on XPS data interpretation. Also, the use of these techniques could find broader applicability in other similar domains; for instance, these approaches could be adapted for use in X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD) studies.  
This study opens up new avenues in the field of materials science data analysis. Looking forward, we anticipate that this methodology could be adapted for other types of spectroscopy or even extended to various data interpretation tasks beyond the field of materials science. As LLMs continue to evolve, the opportunities for integrating AI-driveninterpretations into data analysis processes will only expand, providing exciting prospects for the future.  
Conceptualization, J.d.C. and I.d.Z.; funding acquisition, G.R. and C.T.C.; investigation, I.d.Z. and J.d.C.; methodology, I.d.Z. and J.d.C.; software, J.d.C. and I.d.Z.; supervision, G.R. and C.T.C.; writing--original draft, J.d.C. and I.d.Z.; writing--review and editing, G.R., C.T.C., J.d.C. and I.d.Z. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
Data availability statement: Data are contained within the article.  
The authors declare that they have no conflicts of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript: