Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy
Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy
复制标题
用于 X 射线吸收光谱和 X 射线发射光谱中无偏差化学分类的无监督机器学习
DOI：
10.1039/d1cp02903g
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发表时间：
2021
影响因子：
3.3
通讯作者：
Seidler, Gerald T.
中科院分区：
文献类型：
--
作者：
Tetef, Samantha;Govind, Niranjan;Seidler, Gerald T.
我们报告了一个全面的无监督机器学习的计算研究，用于提取X射线吸收近边结构（XANES）和价核X射线发射光谱（VtC-XES）中的化学相关信息，用于对广泛的硫有机分子系综进行分类。通过逐步减少无监督机器学习算法的约束假设，从主成分分析（PCA）到变分自动编码器（VAE）再到t分布随机邻居嵌入（t-SNE），我们发现对稳定更精细的化学信息的敏感性有所提高。令人惊讶的是，当仅在两个维度上嵌入光谱系综时，t-SNE不仅区分了氧化态和一般硫键合环境，而且还以87%的准确度区分了键合基团的芳香性，并在芳香族或脂肪族子类中识别了电子结构中更精细的细节。我们发现XANES和VtC-XES中的化学信息在性质和内容上非常相似，尽管它们在给定的分子类别内具有不同的灵敏度。我们还讨论了无监督机器学习的进一步努力以及X射线光谱学的监督和无监督机器学习之间的相互作用可能带来的好处。我们的总体结果，即，在没有用户偏见情况下可靠分类以及发现XANES和VtC-XES的意外化学特征的能力，可能推广到其他系统以及其他一维化学光谱。
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 sulphorganic 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 neighbour 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 sulphur bonding environment but also 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 unexpectedly 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.