Machine Learning Approach for Predicting Coordination Numbers from EXAFS Spectra

Abstract: Abstract [Background] X-ray Absorption Fine Structure (XAFS) is a vital technique for structural analysis, widely employed to investigate the oxidation state, coordination environment, and neighboring atom properties of amorphous materials and disordered systems. However, the complexity of XAFS spectra often requires interpretation by experienced researchers, which can still lead to inaccuracies. [Purpose] This study aims to use machine learning approaches to analyze XAFS data and predict the coordination number of absorbing atoms. [Methods] First, a dataset of 13,374 valid EXAFS spectra of fourth-period transition metal elements was sourced from the Materials Project database. Second, this data was utilized to train three machine learning models: neural networks, bagging models, and random forest models. Finally, these models were applied to predict the coordination numbers of the absorbing atoms in the spectra. [Results] The study achieved an average prediction accuracy of approximately 70%. Feature importance analysis revealed that data points within R < 3.0 Å were critical for predictions, consistent with the prominence of short-range atomic interactions in EXAFS theory. [Conclusions] This research enhances the efficiency and reliability of XAFS data analysis by improving model generalizability and interpretability.