Automating core-level spectroscopy calculations: towards high-throughput and machine learning applications

Computational spectroscopy has become a vital tool in materials research, enabling both qualitative and quantitative insights into material properties in close connection with experiments. Here, we present advancements in the automation of ab initio core-level spectroscopy calculations, with a focus on integrating high-throughput computing and machine learning methods to enhance the analysis of experimental spectra. By leveraging first principles simulations, this work explores the electronic and structural characterization of a prototype material, hydrogenated graphene, providing insight into its electronic and structural properties [1]. Our study also demonstrates the use of X-ray Raman scattering (XRS) for analyzing silicon nanoparticle anodes, revealing key components of the solid-electrolyte interphase (SEI) and correlating these with observed capacity loss in lithium-ion batteries [2]. In addition, we develop a robust, modular workflow for ab initio high-throughput X-ray Photoelectron Spectroscopy (XPS) simulations [3]. Furthermore, machine learning models were trained to predict XPS spectra with ab initio accuracy, showcasing their potential to reduce the computational workload required in amorphous-like systems. 

[1] M. G. Betti, D. Marchiani, A. Tonelli, M. Sbroscia, E. Blundo, M. De Luca, A. Polimeni, R. Frisenda, C. Mariani, S. Jeong, Y. Ito, N. Cavani, R. Biagi, P. N.O. Gillespie, M. A. Hernandez Bertran, M. Bonacci, E. Molinari, V. De Renzi, and D. Prezzi.  “Dielectric response and excitations of hydrogenated free-standing graphene”, Carbon Trends 12, 100274 (2023); doi: 10.1016/j.cartre.2023.100274;
[2] M. A. Hernandez Bertran, D. Zapata Dominguez, C. Berhaut, A. Longo, C. Sahle, C. Cavallari, I. Marri, N. Herlin-Boime, E. Molinari, S. Pouget, D. Prezzi, and S. Lyonnard. “Understanding the irreversible lithium loss in silicon anodes using multi-edge X-ray scattering analysis”. Submitted to Chem. Mater.; arXiv: 10.48550/arXiv.2410.05794;
[3] P. N. O. Gillespie, M. A. Hernandez Bertran, X. Wang, G. Pizzi, E. Molinari, and D. Prezzi. “Automated Workflows for Core-Level Spectroscopy Simulation”. Preprint.

Seminar realized in the framework of the funded projects: - BIG-MAP - Battery Interface Genome - Materials Acceleration Platform- GA No. 957189. - 2D-FRONTIERS - Next Generation EU PRIN 2022, GA: 20228879FT