3 papers
physics.chem-ph2026
Graph Neural Network Predictions of Carbon 1s Binding Energies with Near-Experimental Accuracy
Adam E. A. Fouda, Joshua Zhou, Rodrigo Ferreira +10
Graph neural networks are promising architectures for fast, accurate and transferable predictions of core-electron binding energies, which depend on the local bond environment. Her…
physics.comp-ph2026
Equivariant Interatomic Potentials without Tensor Products
Thiago Reschützegger, Sarp Aykent, Gabriel Jacob Perin +5
Foundational machine-learned interatomic potentials have emerged as powerful tools for atomistic simulations, promising near first-principles accuracy across diverse chemical space…
quant-ph2025
Computing band gaps of periodic materials via sample-based quantum diagonalization
Alan Duriez, Pamela C. Carvalho, Marco Antonio Barroca +7
A key objective of computational solid state physics is to predict electronic properties of periodic materials. However, electronic structure simulations based on density functiona…