Vladyka Anton, Sahle Christoph J, Niskanen Johannes
University of Turku, Department of Physics and Astronomy, 20014 Turun yliopisto, Finland.
European Synchrotron Radiation Source, 71 Avenue des Martyrs, 38000 Grenoble, France.
Phys Chem Chem Phys. 2023 Mar 1;25(9):6707-6713. doi: 10.1039/d2cp05420e.
We report a statistical analysis of Ge K-edge X-ray emission spectra simulated for amorphous GeO at elevated pressures. We find that employing machine learning approaches we can reliably predict the statistical moments of the K'' and K peaks in the spectrum from the Coulomb matrix descriptor with a training set of ∼ 10 samples. Spectral-significance-guided dimensionality reduction techniques allow us to construct an approximate inverse mapping from spectral moments to pseudo-Coulomb matrices. When applying this to the moments of the ensemble-mean spectrum, we obtain distances from the active site that match closely to those of the ensemble mean and which moreover reproduce the pressure-induced coordination change in amorphous GeO. With this approach utilizing emulator-based component analysis, we are able to filter out the artificially complete structural information available from simulated snapshots, and quantitatively analyse structural changes that can be inferred from the changes in the K emission spectrum alone.
我们报告了对高压下非晶态GeO模拟的Ge K边X射线发射光谱的统计分析。我们发现，采用机器学习方法，我们可以用大约10个样本的训练集，从库仑矩阵描述符可靠地预测光谱中K''和K峰的统计矩。光谱显著性引导的降维技术使我们能够构建从光谱矩到伪库仑矩阵的近似逆映射。将此应用于系综平均光谱的矩时，我们获得的与活性位点的距离与系综平均值的距离紧密匹配，并且还再现了非晶态GeO中压力诱导的配位变化。通过这种基于模拟器的成分分析方法，我们能够滤除模拟快照中可用的人为完整结构信息，并定量分析仅从K发射光谱变化中推断出的结构变化。
