AutoPas is a C++ library capable of running molecular dynamics simulations with different optimization schemes, which are configurations with differing data structures (i.e. array of structures or structure of arrays), traversal strategies, container types (e.g. linked cells, verlet lists) and optimization techniques (enabling Newton 3 optimization). The current methodology uses an algorithm that tests out the whole search space, which increases day by day. In this thesis, the process of training a machine learning model based on neural networks to create an auto-tuner capable of suggesting the best simulation configuration available to AutoPas is shown. This strategy reduces search time by testing fewer options, and it chooses the optimal configuration with a likelihood of over 99%.      «

AutoPas is a C++ library capable of running molecular dynamics simulations with different optimization schemes, which are configurations with differing data structures (i.e. array of structures or structure of arrays), traversal strategies, container types (e.g. linked cells, verlet lists) and optimization techniques (enabling Newton 3 optimization). The current methodology uses an algorithm that tests out the whole search space, which increases day by day. In this thesis, the process of trainin...     »