1 citations · 1 across the 3 of their papers we have counts for
5 papers
A charge-density machine-learning workflow for computing the infrared spectrum of molecules
Suman Hazra, Urvesh Patil, Stefano Sanvito
We present a machine-learning workflow for the calculation of the infrared spectrum of molecules, and more generally of other temperature-dependent electronic observables. The main…
A general formalism for machine-learning models based on multipolar-spherical harmonics
Michelangelo Domina, Stefano Sanvito
The formulation of descriptors of the local chemical environment, enabling the construction of machine-learning models, is usually obtained by studying the properties of the expans…
Machine-learning semi-local exchange-correlation functionals for Kohn-Sham density functional theory of the Hubbard model
Eoghan Cronin, Rajarshi Tiwari, Stefano Sanvito
The Hubbard model provides a test bed to investigate the complex behaviour arising from electron-electron interaction in strongly-correlated systems and naturally emerges as the fo…
1D Kinetic Energy Density Functionals learned with Symbolic Regression
Michael A. J. Mitchell, Teresa Del Aguila Ferrandis, Stefano Sanvito
Orbital-free density functional theory promises to deliver linear-scaling electronic structure calculations. This requires the knowledge of the non-interacting kinetic-energy densi…
Covariant Jacobi-Legendre expansion for total energy calculations within the projector-augmented-wave formalism
Bruno Focassio, Michelangelo Domina, Urvesh Patil +2
Machine-learning models can be trained to predict the converged electron charge density of a density functional theory (DFT) calculation. In general, the value of the density at a…