In short-range molecular dynamics (MD) simulations, inhomogeneous particle distributions that dynamically change over time require flexible load-balancing methods to achieve good parallel efficiency. We have realized a general framework that can support different load-balancing methods and that can extend existing simulation packages in a minimally invasive way. This is a follow-up to recent work where we integrated it into the MD software ESPResSo to support load-balancing. We have realized a first partitioning strategy based on space-filling curves that can be used for efficient load-balanced multi-physics simulations. In this work we present a new graph-based partitioning strategy that leads to unstructured spatial domain decompositions and integrates well into the existing framework. We apply this to an inhomogeneous soot agglomeration scenario. For several load metrics, graph partitioning leads to better results than space-filling curves. The results indicate that the parallel performance for a given scenario requires a delicate combination of partitioning strategy and load metrics.