Atomic motion captured on-the-fly by machine learning

Description

Physicists of the University of Vienna publish findings on the phase transitions of hybrid perovskites that have the potential to serve as novel solar cell materials

At the atomic scale materials can show a rich palette of dynamic behaviour, which directly affects the physical properties of these materials. For many years, it has been a dream to describe these dynamics in complex materials at various temperatures using computer simulations. Physicists of the University of Vienna have developed an on-the-fly machine-learning method that enables such calculations through direct integration into the quantum mechanics based Vienna Ab-initio Simulation Package (VASP). The versatility of the self-learning method is demonstrated by new findings, published in the journal Physical Review Letters, on the phase transitions of hybrid perovskites. These perovskites are of great scientific interest due to their potential in solar energy harvesting and other applications. ....