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In the dynamic field of materials science, the quest to find optimalstructures with low potential energy is of great significance. Over the pasttwo decades, the minima hopping algorithm has emerged as a successful tool inthis pursuit. We present a robust, user friendly and efficient implementationof the minima hopping algorithm as a Python library, enhancing in this way theglobal structure optimization simulations significantly. Our implementationsignificantly accelerates the exploration the potential energy surfaces,leveraging an MPI parallelization scheme that allows for multi levelparallelization. In this scheme, multiple minima hopping processes are runningsimultaneously communicating their findings to a single database and,therefore, sharing information with each other about which parts of thepotential energy surface have already been explored. Also multiple featuresfrom several existing implementations such as variable cell shape moleculardynamics and combined atomic position and cell geometry optimization for bulksystems, enhanced temperature feedback and fragmentation fixing for clustersare included in this implementation. Finally, this implementation takesadvantage of the Atomic Simulation Environment (ASE) Python library allowingfor high flexibility regarding the underlying energy and force evaluation.

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