DMFT on the 2D Hubbard-Holstein model produces two Fermi-resonance peaks in electronic friction missed by MFT, with EF-LD simulations revealing substantial differences in electron population dynamics.
Proton-coupled electron transfer in solution, proteins, and electrochemistry.The Journal of Physical Chemistry B, 112 (45):14108–14123
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DenSNet learns the Hohenberg-Kohn map to electron density with equivariant networks and delta-learning, then maps density to energy, producing stable MD trajectories whose infrared spectra match experiment and DFT on ethanol, ethanethiol, resorcinol, and polythiophene oligomers.
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A DMFT approach to evaluate electronic frictional effects near solid surfaces of strongly correlated systems
DMFT on the 2D Hubbard-Holstein model produces two Fermi-resonance peaks in electronic friction missed by MFT, with EF-LD simulations revealing substantial differences in electron population dynamics.
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Enhancing molecular dynamics with equivariant machine-learned densities
DenSNet learns the Hohenberg-Kohn map to electron density with equivariant networks and delta-learning, then maps density to energy, producing stable MD trajectories whose infrared spectra match experiment and DFT on ethanol, ethanethiol, resorcinol, and polythiophene oligomers.