Roadmap on Machine Learning in Electronic Structure

Kulik et al., IOP Electronic Structure DOI: 10.1088/2516-1075/ac572f (2022)

"A perspective roadmap that covers the present role and future perspective of machine learning in materials property prediction, the construction of accurate force fields, the solution of the many-body problem, and big data challenges."

Coexistence of carbonyl and ether groups on oxygen-terminated (110)-oriented diamond surfaces

Shayanthan Chaudhuri, Samuel J. Hall, Benedikt P. Klein, Marc Walker, Andrew J. Logsdail, Julie V. Macpherson, Reinhard J. Maurer, Communications Materials 3, 6 (2022)

"Here, we determine the oxygenation state of the (110) surface using a combination of density functional theory calculations and X-ray photoelectron spectroscopy experiments. We report the fabrication of the highest-quality (100)-oriented diamond crystal surface to date. We further propose a mechanism for the formation of the hybrid carbonyl-ether phase and rationalize its high stability. "

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

J. Matthias Kahk, Georg S. Michelitsch, Reinhard J. Maurer, Karsten Reuter, Johannes Lischner, J. Phys. Chem. Lett. 12, 9353-9359 (2021)

"We present an approach to calculate accurate core electron binding energies of a variety of materials based on Delta-self-consistent-field calculations that are referenced to the valence band maximum. We further show that the resulting simulations provide excellent agreement with experimental X-ray photoemission spectroscopy data."

Perspective on integrating machine learning into computational chemistry and materials science

Julia Westermayr, Michael Gastegger, Kristof T. Schütt, Reinhard J. Maurer, J. Chem. Phys. 154, 230903 (2021)

"As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML."