Machine Learning to Predict Spectral Anisotropy in Valence-to-Core X-ray Emission Spectroscopy
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918349351419904 |
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| author | Cardot, Charles A. Tichenor, John R. Shjandemaar, Seth M. Kas, Josh J. Seidler, Gerald T. Rehr, John J. |
| author_facet | Cardot, Charles A. Tichenor, John R. Shjandemaar, Seth M. Kas, Josh J. Seidler, Gerald T. Rehr, John J. |
| contents | Polarization analysis in x-ray spectroscopy provides an orientation dependent sensitivity to local bonding environments. For a cluster of atoms, polarization sensitivity is most often discussed through the lens of point group symmetries. However, this is a discrete, qualitative method of classifying clusters, and it does little to indicate the degree of spectral anisotropy. Here we adopt a random forest model for quantitative prediction of spectral anisotropy. Its input relies on simplified local geometric and chemical information that can be obtained from any crystal structure file. The model is trained on over 10,000 experimentally realized transition metal structures from the Materials Project, with the target being VtC-XES calculated using the real space Green's function code FEFF. We find that the model can strongly predict the degree of spectral anisotropy, with the primary factors being derived from the spatial moments of ligands in a cluster. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_00242 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning to Predict Spectral Anisotropy in Valence-to-Core X-ray Emission Spectroscopy Cardot, Charles A. Tichenor, John R. Shjandemaar, Seth M. Kas, Josh J. Seidler, Gerald T. Rehr, John J. Strongly Correlated Electrons Polarization analysis in x-ray spectroscopy provides an orientation dependent sensitivity to local bonding environments. For a cluster of atoms, polarization sensitivity is most often discussed through the lens of point group symmetries. However, this is a discrete, qualitative method of classifying clusters, and it does little to indicate the degree of spectral anisotropy. Here we adopt a random forest model for quantitative prediction of spectral anisotropy. Its input relies on simplified local geometric and chemical information that can be obtained from any crystal structure file. The model is trained on over 10,000 experimentally realized transition metal structures from the Materials Project, with the target being VtC-XES calculated using the real space Green's function code FEFF. We find that the model can strongly predict the degree of spectral anisotropy, with the primary factors being derived from the spatial moments of ligands in a cluster. |
| title | Machine Learning to Predict Spectral Anisotropy in Valence-to-Core X-ray Emission Spectroscopy |
| topic | Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2602.00242 |