A deep neural network for valence-to-core X-ray emission spectroscopy
A deep neural network for valence-to-core X-ray emission spectroscopy
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DOI：
10.1080/00268976.2022.2123406
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发表时间：
2022-09
影响因子：
1.7
通讯作者：
T. Penfold;C. Rankine
中科院分区：
文献类型：
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作者：
T. Penfold;C. Rankine
摘要在这篇文章中，我们扩展了我们的XANESNET深度神经网络（DNN）来预测第一行过渡金属K边价核X射线发射（VtC-XES）光谱的线形。我们表明，尽管VtC-XES的电子结构的研究中的系统的强烈的敏感性-DNN可以再现的主要光谱特征时，编码为加权原子中心对称函数（wACSF）的特征向量仅从局部协调几何的过渡金属络合物。随后，我们实施和评估三种方法来评估VtC-DNN预测的不确定性：深度集成，蒙特-卡罗辍学，和引导响应。我们表明，自举响应提供了最好的性能时，评估“坚持”的测试数据，也表明了很强的相关性之间的不确定性，它预测的目标和预测的VtC-XES光谱之间发生的错误。最后，我们展示了实际性能的应用程序看不见的过渡金属络合物在整个第一行（钛锌）。图形摘要
ABSTRACT In this Article, we extend our XANESNET deep neural network (DNN) to predict the lineshape of first-row transition metal K-edge valence-to-core X-ray emission (VtC-XES) spectra. We demonstrate that – despite the strong sensitivity of VtC-XES to the electronic structure of the system under study – the DNN can reproduce the main spectral features from only the local coordination geometry of the transition metal complexes when encoded as a feature vector of weighted atom-centred symmetry functions (wACSF). We subsequently implement and evaluate three methods for assessing uncertainty in the predictions made by the VtC-DNN: deep ensembles, Monte-Carlo dropout, and bootstrap resampling. We show that bootstrap resampling provides the best performance when evaluated on ‘held-out’ testing data, and also demonstrates a strong correlation between the uncertainty it predicts and the error occurring between the target and predicted VtC-XES spectra. Finally, we demonstrate practical performance by application to unseen transition metal complexes across the entire first-row (Ti–Zn). GRAPHICAL ABSTRACT