Golze Dorothea, Hirvensalo Markus, Hernández-León Patricia, Aarva Anja, Etula Jarkko, Susi Toma, Rinke Patrick, Laurila Tomi, Caro Miguel A
Faculty of Chemistry and Food Chemistry, Technische Universität Dresden, 01062 Dresden, Germany.
Department of Applied Physics, Aalto University, 02150 Espoo, Finland.
Chem Mater. 2022 Jul 26;34(14):6240-6254. doi: 10.1021/acs.chemmater.1c04279. Epub 2022 Jul 13.
We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which X-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines density functional theory (DFT) with and uses kernel ridge regression for the ML predictions. We apply the new approach to disordered materials and small molecules containing carbon, hydrogen, and oxygen and obtain qualitative and quantitative agreement with experiment, resolving spectral features within 0.1 eV of reference experimental spectra. The method only requires the user to provide a structural model for the material under study to obtain an XPS prediction within seconds. Our new tool is freely available online through the XPS Prediction Server.
我们提出了一种用于核心电子结合能计算预测的定量精确机器学习（ML）模型，由此可轻松获得X射线光电子能谱（XPS）。我们的模型将密度泛函理论（DFT）与[此处原文缺失部分内容]相结合，并使用核岭回归进行ML预测。我们将这种新方法应用于含碳、氢和氧的无序材料及小分子，获得了与实验的定性和定量一致性，能分辨出与参考实验光谱相差0.1 eV以内的光谱特征。该方法仅要求用户提供所研究材料的结构模型，就能在数秒内获得XPS预测结果。我们的新工具可通过XPS预测服务器在线免费获取。
