2 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.chem-ph2025
MC-PDFT Nuclear Gradients and L-PDFT Energies with Meta and Hybrid Meta On-Top Functionals for Ground- and Excited-State Geometry Optimization and Vertical Excitation Energies
Matthew R. Hennefarth, Younghwan Kim, Bhavnesh Jangid +4
Multiconfiguration pair-density functional theory (MC-PDFT) is a post-MCSCF multireference electronic-structure method that explicitly models strong electron correlation, and linea…