Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and <i>GW</i>
Dorothea Golze, Markus Hirvensalo, Patricia Hernández-León, Anja Aarva, Jarkko Etula, Toma Susi, Patrick Rinke, Tomi Laurila, A. Miguel
Abstract
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.
Topics & Concepts
Density functional theoryCore (optical fiber)ElectronCarbon fibersCore electronElectron densityMaterials scienceStatistical physicsComputer scienceComputational physicsPhysicsAlgorithmComputational chemistryChemistryQuantum mechanicsOpticsComposite numberMachine Learning in Materials ScienceElectron and X-Ray Spectroscopy TechniquesAdvanced Chemical Physics Studies