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Found 2 papers, 1 papers with code
Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function
Integrating deep learning with the search for new electron-phonon superconductors represents a burgeoning field of research, where the primary challenge lies in the computational intensity of calculating the electron-phonon spectral function, $\alpha^2F(\omega)$, the essential ingredient of Midgal-Eliashberg theory of superconductivity.
Implicit self-consistent description of electrolyte in plane-wave density-functional theory
In this work we describe a computationally efficient model where the electrode part of the interface is modeled at the density functional theory (DFT) level and the electrolyte part is represented through an implicit model based on the Poisson-Boltzmann equation.
Materials Science
