Performing large-scale quantum molecular dynamics simulations

In this talk I will discuss techniques and workflows targeted at the simulation of molecular dynamics protocols based on ab initio calculations, machine-learned (ML) potentials of ab initio quality and including quantum nature of the nuclei. I will discuss aspects of electronic-structure code efficiency in HPC architectures to generate data in order to train ML models and recent improvements in the i-PI code which decrease communication overhead when running over many HPC nodes, allowing to reach nanoseconds of molecular dynamics simulation per day with ML potentials.