Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW

[Image: see text] 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 GW and use...

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Detalles Bibliográficos
Autores principales: Golze, Dorothea, Hirvensalo, Markus, Hernández-León, Patricia, Aarva, Anja, Etula, Jarkko, Susi, Toma, Rinke, Patrick, Laurila, Tomi, Caro, Miguel A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9330771/
https://www.ncbi.nlm.nih.gov/pubmed/35910537
http://dx.doi.org/10.1021/acs.chemmater.1c04279

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9330771/
https://www.ncbi.nlm.nih.gov/pubmed/35910537
http://dx.doi.org/10.1021/acs.chemmater.1c04279

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