## 4 Conclusions and Outlook
Regarding the energy function, full quantum mechanical evaluations of the energy are computationally very expensive and thus always a stretch for global landscape exploration. Nevertheless, energy functions with a good balance between accuracy and computational efficiency, e.g., based on density functional-based tight-binding (DFTB) [46], are available and have been successfully employed to globally explore energy landscapes of chemical systems [47]. The issue of the configuration space that needs to be smoothly but efficiently explored is a much more subtle one. We note that it will also appear in any application of IGLOO as a general global optimization algorithm for cost function landscapes drawn not only from robotics and chemistry but also from physics, biology, or economics. The concern here is the question whether the (T)RRT exploration methodology can be employed beyond compact state spaces such as the \(n\)-dimensional torus for a chain molecule or a multi-link robot arm where \(n\) angular variables characterize the microstates of the system. One class of systems for which (T)RRT appears to be very suitable are the landscapes of periodic approximants to crystalline solids, where the atom coordinates also exhibit a torus-like topology. While the threshold control and the stochastic quenches have proven their worth in many global landscapes studies, testing the (T)RRT feature of IGLOO for other classes of energy landscapes will be an important direction of future research. The extension of IGLOO to a large variety of cost function problems will, of course, be accompanied by the fine-tuning of the current version of IGLOO, optimizing the parameters of the algorithm and possibly developing adaptive mechanisms for selection and modifying the IGLOO's control parameters based on information gathered about the landscape. Finally, the graph generation and exploration feature of the (T)RRT part of IGLOO should be a valuable tool for an efficient search for transition path candidates on the energy landscape. Combining this with the measurement of probability flows provided by the threshold algorithm could result in a promising approach for efficiently gaining deeper insights into the barrier structure of an energy landscape, which controls the stability of the minima configurations of a chemical system and governs the transformations among these structures and phases. Another interesting direction for future work would be the extension of IGLOO to address general problems with high-dimensional state spaces. Up to now, we have successfully tested IGLOO on problems involving several dozen variables, but we can imagine difficulties in tackling problems involving hundreds or thousands of variables, which represent a huge challenge for global exploration. This extension may require a sophisticated parallel implementation of the algorithm. For the investigation presented in this work, we employed a basic multi-threading strategy exploiting the shared-memory architecture of current multi-core CPUs. The execution on larger computer clusters would need a more in-depth strategy, avoiding unnecessary communication between processes. Our previous work on the parallelization of RRT-like algorithms based on an automatic subdivision of space [48] could be a good starting point. In addition to the \"high-level\" parallelization of the algorithm for efficient execution on multiple CPUs, one can also envisage GPU-accelerated calculation of certain \"low-level\" functions inside the algorithm, such as energy or distance calculations. Thus, we expect IGLOO to prove to be a highly versatile tool for global optimization tasks and for the global exploration of complex energy landscapes in the future. Conceptualization, J.C.S. and J.C.; Funding acquisition, C.M.-R., N.T. and J.C.; Investigation, W.M. ; Methodology, W.M., A.C. and J.C.; Resources, N.T. and J.C.; Software, W.M., A.C. and J.C.; Supervision C.M.-R., N.T. and J.C.; Visualization: W.M., C.M.-R., N.T. and J.C.; Writing--original draft: W.M., J.C.S., N.T.