3 papers
cond-mat.mtrl-sci2026
Machine-learning approach for the phase stability and mechanical properties of disordered alloys at finite temperature
Rutchapon Hunkao, Urvesh Patil, Stefano Sanvito
The prediction of stable alloys forming solid-state solutions across large portions of the composition space is a serious theoretical challenge, since one has to evaluate the Gibbs…
physics.chem-ph2025
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…
cond-mat.mtrl-sci2024
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…