Understanding Anharmonic Effects on Hydrogen Desorption Characteristics of Mg$_n$H$_{2n}$ Nanoclusters by ab initio trained Deep Neural Network

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.