Unsupervised Machine Learning for Unbiased Chemical Classification in X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy

Abstract

We report a comprehensive computational study of unsupervised machine learning for extraction of chemically relevant information in X-ray absorption near edge structure (XANES) and in valence-to-core X-ray emission spectra (VtC-XES) for classification of a broad ensemble of sulforganic molecules. By progressively decreasing the constraining assumptions of the unsupervised machine learning algorithm, moving from principal component analysis to a variational autoencoder to t-distributed stochastic neighbor embedding (t-SNE), we find improved sensitivity to steadily more refined chemical information. Surprisingly, even in merely two dimensions, t-SNE distinguishes not just oxidation state and general sulfur bonding environment but the aromaticity of the bonding radical group with 87% accuracy as well as identifying even finer details in electronic structure within aromatic or aliphatic sub-classes. We find that the chemical information in XANES and VtC-XES is very similar, although they exhibit an unexpected tendency to have different sensitivity within a given molecular class.

Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions

“…105 Significant efforts by several groups have focused on XANES data analysis for a range of different materials. 7,8,114,115,[106][107][108][109][110][111][112][113] In our opinion, these studies lay the foundation for the development of analogous methods for EXAFS analysis. data.…”

Interpretable Machine learning based coordination motif identification scheme from X-ray absorption near-edge structure spectroscopy XANES

“…For instance, Timoshenko applied convolutional neural networks in 2018 to extract radial distribution function(RDF) from two-dimensional XAS wavelet data [3];Zheng et al used ensemble machine learning algorithm [6]. In addition, unsupervised learning algorithms are also applied to XANES analysis [5,10].Existing researches mostly focus on the use of training sets built for specific samples. Machine learning based on this is aimed at improving its structural analysis performance, but its generalization is limited while obtaining powerful structure analysis ability.…”