Excitations in Realistic Materials using Yambo on Massively Parallel Architectures
- Conor Hogan (CNR-ISM, Rome and University of Rome Tor Vergata, Italy)
- Andrea Marini (National Research Council, Rome, Italy)
- Myrta Gruning (Queen's University Belfast, United Kingdom)
- Davide Sangalli (CNR-ISM, Uos di Montelibretti, Italy)
- Andrea Ferretti (CNR-Institute of Nanoscience, Modena, Italy)
- Daniele Varsano (S3, CNR Istituto di Nanoscienze, Italy)
- Maurizia Palummo (University of Rome II 'Tor Vergata', Italy)
- Pedro Melo (Université de Liège, Belgium)
- Margherita Marsili (S3, CNR Istituto di Nanoscienze, Italy)
*** Deadline for Registration: 27th February!! ***
Characterization and engineering of complex systems in material science often requires an accurate description of their excited-state properties. This tutorial will provide training in the theory and practice of computing electronic and optical excitations within density functional and Green’s function approaches, and in the application of these techniques to the study of realistic and challenging systems using the Yambo code within a massively-parallel environment.
Yambo is a GPL-distributed code for Many-Body calculations in solid state and molecular physics, and is part of the ETSF software suite. Interfaced with two key GPL-distributed DFT engines (quantum-ESPRESSO and Abinit), Yambo counts about 300 active users and over 200 publications, and boasts several advanced or unique features, including spin-polarized excitations and electron-phonon coupling.
The current trends in high-performance computing are towards massively parallel, distributed memory architectures, which require both specially tuned software as well as skilled users to obtain efficient results. This tutorial will mark the release of a completely new version of Yambo which is adapted for massively-parallel computing architectures, allowing truly realistic systems to be tackled.
Our goal is to thus equip students with the fundamental knowledge, practical skills and computational tools needed to tackle today’s challenging problems in materials science.