Prange Micah P, Govind Niranjan, Stinis Panos, Ilton Eugene S, Howard Amanda A
Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.
Department of Chemistry, University of Washington, Seattle, Washington 98195, United States.
J Phys Chem A. 2025 Jan 9;129(1):346-355. doi: 10.1021/acs.jpca.4c05612. Epub 2024 Dec 24.
The fact that the photoabsorption spectrum of a material contains information about the atomic structure, commonly understood in terms of multiple scattering theory, is the basis of the popular extended X-ray absorption spectroscopy (EXAFS) technique. How much of the same structural information is present in other complementary spectroscopic signals is not obvious. Here we use a machine learning approach to demonstrate that within theoretical models that accurately predict the EXAFS signal, the extended near-edge region does indeed contain the EXAFS-accessible structural information. We do this by exhibiting deep operator neural networks (DeepONets) that have learned the relationship between the extended and near edge portions of the X-ray absorption spectrum to predict the former from the latter. We find that we can accurately predict the EXAFS spectrum between 6 and 14 Å from the first 6 Å (≈100 eV) of the absorption spectrum of Cu substitutional defects in the Fe mineral hematite (α-FeO). This surprising finding implies that theoretical analyses of X-ray absorption spectra could be implemented that extract the conclusions as high-quality EXAFS studies from spectra collected over a much smaller range of photon energies. This relaxes a host of experimental limitations related to the X-ray source and measurement sample, including collection time, minimum dopant concentration, source brilliance, and energy range. We describe the theoretical data sets and DeepONet construction and show that the resulting DeepONets produce EXAFS that recovers linear combination fits to experimental data with accuracy approaching the original ab initio calculations. We discuss the implications of our findings for minor constituent characterization and for understanding the information content of spectroscopic data more broadly, including how this approach might be applied to measured experimental spectra. To encourage similar efforts, the simulated X-ray spectra, machine learning, and fitting code are publicly available.
材料的光吸收光谱包含有关原子结构的信息，这一点通常根据多重散射理论来理解，它是广受欢迎的扩展X射线吸收光谱（EXAFS）技术的基础。在其他互补光谱信号中存在多少相同的结构信息并不明显。在这里，我们使用机器学习方法来证明，在准确预测EXAFS信号的理论模型中，扩展近边区域确实包含EXAFS可获取的结构信息。我们通过展示深度算子神经网络（DeepONets）来做到这一点，该网络已经学习了X射线吸收光谱的扩展部分和近边部分之间的关系，以便从后者预测前者。我们发现，我们可以根据铁矿物赤铁矿（α-FeO）中铜替代缺陷吸收光谱的前6 Å（≈100 eV）准确预测6至14 Å之间的EXAFS光谱。这一惊人发现意味着，可以对X射线吸收光谱进行理论分析，从在小得多的光子能量范围内收集的光谱中提取出与高质量EXAFS研究相同的结论。这放宽了许多与X射线源和测量样品相关的实验限制，包括采集时间、最小掺杂剂浓度、源亮度和能量范围。我们描述了理论数据集和DeepONet的构建，并表明由此产生的DeepONets生成的EXAFS能够恢复与实验数据的线性组合拟合，其精度接近原始的从头计算。我们讨论了我们的发现对微量成分表征以及更广泛地理解光谱数据信息内容的影响，包括这种方法如何应用于实测实验光谱。为鼓励类似的研究，模拟的X射线光谱、机器学习和拟合代码已公开提供。
