Fig. 7.

Symmetry-driven mesostructured material discovery pipeline. To obtain mesostructured materials with a set of desired properties, we suggest the following automated discovery pipeline. We start from a library of self-assembled structures, which is scanned for candidates matching symmetry requirements for a set of target properties. This requires us to automatically determine the space group of each structure: a script space_group.py does this job for structures represented as a skeletal graph (SI Appendix). A best candidate for the initial structure is then selected, and its properties are numerically computed. For example, we compute the band structure, from which the topological charges of the Weyl points (if any) are determined by a script weyl_charge.py. An effective description is then extracted from the numerical data: here, we need to determine the irreducible representations of the numerical eigenvectors, a job performed by the script irreps.py (SI Appendix). The effective description then allows us to determine which modifications should lead to the desired properties (for example, through a symmetry reduction). Here, this step could also be automated using https://github.com/greschd/kdotp-symmetry. Finally, the properties of the modified structure are numerically determined and compared with the desired properties. In case of failure, a new initial structure is selected from the library, and the process is iterated.