Core Electron Binding Energies in Solids from Periodic All-Electron Δ-Self-Consistent-Field Calculations

Kahk, Juhan Matthias; Michelitsch, Georg S.; Maurer, Reinhard J.; Reuter, Karsten; Lischner, Johannes

doi:10.1021/acs.jpclett.1c02380

Cited by 26 publications

(57 citation statements)

References 79 publications

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“… 17 − 20 Recently, ΔSCF calculations with meta-GGAs were, in combination with finite-size correction schemes, also successfully applied to simple solids. 21 …”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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 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.

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“… 17 − 20 Recently, ΔSCF calculations with meta-GGAs were, in combination with finite-size correction schemes, also successfully applied to simple solids. 21 …”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…It has been recently shown that the SCAN functional yields excellent absolute and relative CLBEs for molecules 17 and solids. 19 We find that this is also true for the ETFA molecule.…”

Section: Core65 Benchmarkmentioning

confidence: 52%

“…The most popular DFT-based approach is the Delta self-consistent field (∆SCF) method, 13 which has been thoroughly benchmarked. [14][15][16][17][18][19] While high accuracy can be achieved with these approaches, the explicit optimization of a core-ionized wave function leads to conceptual problems, e.g., regarding periodicity, constraining spin-orbit coupled states or, in the case of DFT, deteriorating accuracy for larger structures, which was already discussed and demonstrated elsewhere. [20][21][22][23][24] An explicit orbital optimization of core-ionized systems and the related conceptual issues are avoided in response theories, where electron propagators are applied to transform the ground into an excited state.…”

Section: Introductionmentioning

confidence: 99%

Benchmark of $\boldsymbol{GW}$ Methods for Core-Level Binding Energies

Li¹,

Ye²,

Rinke³

et al. 2022

Preprint

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The GW approximation has recently gained increasing attention as a viable method for the computation of deep core-level binding energies as measured by X-ray photoelectron spectroscopy (XPS). We present a comprehensive benchmark study of different GW methodologies (starting-point optimized, partial and full eigenvalue-self-consistent, Hedin shift and renormalized singles) for molecular inner-shell excitations. We demonstrate that all methods yield a unique solution and apply them to the CORE65 benchmark set and ethyl trifluoroacetate. Three GW schemes clearly outperform the other methods for absolute core-level energies with a mean absolute error of 0.3 eV with respect to experiment. These are partial eigenvalue self-consistency, in which the eigenvalues are only updated in the Green's function, single-shot GW calculations based on an optimized hybrid functional starting point and a Hedin shift in the Green's function. While all methods reproduce the experimental relative binding energies well, the eigenvalue self-consistent schemes and the Hedin shift yield with mean errors < 0.2 eV the best results.

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“…Beyond ∆SCF approaches, the XPS spectra of periodic systems have also been computed by GW methods [90,91]. We find of particular interest the work Zhu et al [90] where an implementation based on crystalline Gaussian Basis set for the computation of core-electrons BEs on periodic systems in the GW scheme, is presented.…”

Section: Computational Tools For the Computation Of Xps Spectra: Firs...mentioning

confidence: 99%

Peculiar opportunities given by XPS spectroscopy for the clinician

Brigiano¹,

Bazin²,

Tielens³

2022

Comptes Rendus. Chimie

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X-ray Photoelectron Spectroscopy (XPS) constitutes an elegant way to describe the chemical characteristics of the surface of biological materials. It is thus a unique approach to decipher the interaction between biological materials and tissues. In the case of medical implants, it is thus possible to understand its biocompatibility as well as its integration in the body which can be wanted in the case of prothesis or avoided in the case of JJ-stents. More precisely, XPS can bring valuable information of the interaction between physiological calcification (here bone) and the prosthesis as well as the interaction between pathological calcifications (lithiasis) and the JJ-stent. This mini overview is dedicated to two communities, the physical chemists and the clinicians. In the first part of this overview, after an introduction on the basic principles of XPS, we focus on the theoretical techniques adopted for the computation of XPS spectra of materials.The second part, dedicated to clinicians, describes the use of XPS for the characterization of biological materials. We report which kind of chemical information can be gained by this surfacesensitive technique and how this information has a relevant impact on medical applications.Through different examples, we show that XPS is a strong and very useful tool, and thus receiving a crucial place in medical research.

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Core Electron Binding Energies in Solids from Periodic All-Electron Δ-Self-Consistent-Field Calculations

Cited by 26 publications

References 79 publications

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

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

Benchmark of $\boldsymbol{GW}$ Methods for Core-Level Binding Energies

Peculiar opportunities given by XPS spectroscopy for the clinician

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