Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining density-functional theory and $\mathbf{GW}$

Abstract

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 materials and 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.

References in corpus (13)

• Machine-learning based interatomic potential for amorphous carbon
• The GW compendium: A practical guide to theoretical photoemission spectroscopy
• Towards GW Calculations on Thousands of Atoms
• Highly Accurate Prediction of Core Spectra of Molecules at Density Functional Theory Cost: Attaining sub eV Error from a Restricted Open-Shell Kohn-Sham Approach
• Classification of Local Chemical Environments from X-ray Absorption Spectra using Supervised Machine Learning
• Cubic-scaling all-electron GW calculations with a separable density-fitting space-time approach
• Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
• Low-scaling $GW$ with benchmark accuracy and application to phosphorene nanosheets
• Physically inspired deep learning of molecular excitations and photoemission spectra
• All-electron periodic $G_0{W}_0$ implementation with numerical atomic orbital basis functions: algorithm and benchmarks
• Calculation of the graphene C 1$\text{s}$ core level binding energy
• Relativistic correction scheme for core-level binding energies from $GW$
• Determining electronic properties from L-edge X-ray absorption spectra of transition metal compounds with artificial neural networks

Cited by in corpus (13)

• How to validate machine-learned interatomic potentials
• Accelerating core-level $GW$ calculations by combining the contour deformation approach with the analytic continuation of $W$
• Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization
• Machine Learning and Data-Driven Methods in Computational Surface and Interface Science
• Graph-neural-network predictions of solid-state NMR parameters from spherical tensor decomposition
• Cluster-based multidimensional scaling embedding tool for data visualization
• Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C
• Automated computational workflows for muon spin spectroscopy
• Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework
• Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid
• Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations
• A charge-density machine-learning workflow for computing the infrared spectrum of molecules
• Efficient $GW$ band structure calculations using Gaussian basis functions and application to atomically thin transition-metal dichalcogenides