Highlights Keywords Materials, Energy, Manufacturing & Engineering, Mechanical Engineering Technologies used ML Challenge The central challenge of the XCALE project was adapting various parts of atomistic machine learning simulations, which had been developed in the context of pre-existing CPU-based codes, to make them compatible with efficient execution on GPUs. This is necessary to adapt to the changing paradigm of high-performance computing, based on accelerators, and be able to make use of (pre)exascale supercomputers. For this … Continue reading Success Story: XCALE