no code implementations • 27 Nov 2021 • Andrea Pedrielli, Paolo E. Trevisanutto, Lorenzo Monacelli, Giovanni Garberoglio, Nicola M. Pugno, Simone Taioli
In order to increase the size of NPs toward experiments of hydrogen desorption from MgH$_2$ we devise a computationally effective Machine Learning model trained to accurately determine the forces and total energies (i. e. the potential energy surfaces), integrating the latter with the SSCHA model to fully include the anharmonic effects.
no code implementations • 30 Nov 2020 • Lorenzo Monacelli, Francesco Mauri
We apply perturbation theory around the static SCHA solution and derive an algorithm to compute efficiently quantum dynamical response functions.
Statistical Mechanics Atomic Physics Computational Physics Quantum Physics
no code implementations • 27 Jul 2019 • Ion Errea, Francesco Belli, Lorenzo Monacelli, Antonio Sanna, Takashi Koretsune, Terumasa Tadano, Raffaello Bianco, Matteo Calandra, Ryotaro Arita, Francesco Mauri, José A. Flores-Livas
The relevance of quantum fluctuations in the energy landscape found here questions many of the crystal structure predictions made for hydrides within a classical approach that at the moment guide the experimental quest for room-temperature superconductivity [4, 5, 6].
Superconductivity Materials Science