Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function. (arXiv:2401.16611v1 [cond-mat.supr-con])

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. To overcome this
challenge, we adopt a two-step approach. First, we compute $\alpha^2F(\omega)$
for 818 dynamically stable materials. We then train a deep-learning model to
predict $\alpha^2F(\omega)$, using an unconventional training strategy to
temper the model's overfitting, enhancing predictions. Specifically, …

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