Performing highly efficient Minima Hopping structure predictions using the Atomic Simulation Environment (ASE)

In materials science, the quest to find stable low energy structures is of great significance. Over the past two decades, the Minima Hopping algorithm has emerged as a successful tool in this pursuit. We present a robust, user friendly and efficient implementation of the Minima Hopping algorithm as a Python library. Our implementation significantly accelerates the exploration the potential energy surfaces, leveraging an MPI parallelization scheme that allows for multi level parallelization. In this scheme, multiple Minima Hopping processes are running simultaneously communicating their findings to a single database and, therefore, sharing information with each other about which parts of the potential energy surface have already been explored. Also multiple features from several existing implementations such as variable cell shape molecular dynamics and combined atomic position and cell geometry optimization for bulk systems, enhanced temperature feedback and fragmentation fixing for clusters are included in this implementation. Finally, this implementation takes advantage of the Atomic Simulation Environment (ASE) Python library allowing for high flexibility regarding the underlying energy and force evaluation.