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physics.comp-ph2024★ 1 cited
Solving deep-learning density functional theory via variational autoencoders
Emanuele Costa, Giuseppe Scriva, Sebastiano Pilati
In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data. However, problematic instabilit…
physics.comp-ph2022★ 6 cited
Deep learning density functionals for gradient descent optimization
Emanuele Costa, Giuseppe Scriva, Rosario Fazio +1
Machine-learned regression models represent a promising tool to implement accurate and computationally affordable energy-density functionals to solve quantum many-body problems via…