论文标题
通过人工神经网络确定L-EDGE X射线吸收光谱的电子性能
Determining electronic properties from L-edge X-ray absorption spectra of transition metal compounds with artificial neural networks
论文作者
论文摘要
在L边探头的2p-电子探针中，X射线吸收光谱转变为无占用的D园。应用于过渡金属原子，该实验技术可以提供有关D型电子结构的有价值信息。但是，多重效应，自旋轨道耦合，大量可能的过渡可能会导致2p XAS光谱的性质相当涉及，这通常会使信息直接从中直接提取。在此，提出了在2P XAS模型的模拟光谱上训练的人工神经网络，可以直接确定有关原子能的信息以及从L边缘X射线吸收光谱的D型态的电子构型的信息。此外，人工神经网络（ANN）的适应性允许扩展其能力，从2p XAS光谱中获取有关电子基态和核心孔寿命的信息，以及结合外部因素，例如温度和实验卷积，这些因素可能影响光谱特征的细节。讨论了光谱中噪声和背景贡献对ANN的准确性的影响，并在过渡金属化合物的实验光谱（包括金属有机分子和金属氧化物）上进行了验证。
X-ray absorption spectroscopy at the L-edge probes transitions of 2p-electrons into unoccupied d-states. Applied to transition metal atoms, this experimental technique can provide valuable information about the electronic structure of d-states. However, multiplet effects, spin-orbit coupling, a large number of possible transitions can cause a rather involved nature of 2p XAS spectra, which can often complicate extracting of information directly from them. Here, artificial neural networks trained on simulated spectra of a 2p XAS model Hamiltonian are presented that can directly determine information about atomic properties and the electronic configuration of d-states from L-edge X-ray absorption spectra. Moreover, the adaptable nature of artificial neural networks (ANNs) allows extending their capability to obtain information about the electronic ground state and core hole lifetimes from 2p XAS spectra as well as to incorporate external factors, such as temperature and experimental convolution that can affect details in spectral features. The effects of noise and background contributions in spectra on the accuracy of ANNs are discussed and the method is validated on experimental spectra of transition metal compounds, including metal-organic molecules and metal oxides.
