November 21, 2021
Journal Article

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 (PCA) to a variational autoencoder (VAE) to t-distributed stochastic neighbor embedding (t-SNE), we find improved sensitivity to steadily more refined chemical information. Surprisingly, when embedding the ensemble of spectra 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 in character and content, although they exhibit an unexpected tendency to have different sensitivity within a given molecular class. We also discuss likely benefits from further effort with unsupervised machine learning and from the interplay between supervised and unsupervised machine learning for X-ray spectroscopies. Our overall results, i.e., the ability to reliably classify without user bias and to discover unexpected chemical signatures for XANES and VtC-XES, likely generalize to other systems as well as to other one-dimensional chemical spectroscopies.

Published: November 21, 2021

Citation

Tetef S., N. Govind, and G.T. Seidler. 2021. "Unsupervised Machine Learning for Unbiased Chemical Classification in X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy." Physical Chemistry Chemical Physics 23, no. 41:23586-23601. PNNL-SA-163242. doi:10.1039/D1CP02903G