Crystalline materials are solids composed of microscopic repeating units arranged in a regular, periodic array. At the smallest scales, the repeat units are formed from just a handful of atomic/molecular 'building blocks' arranged with a well-defined symmetry. Like construction bricks, these building blocks can assemble into a plurality of structures (polymorphs), providing materials with different physical and chemical properties. A key characteristic of molecular and ionic crystalline solids is that their building blocks are held together by non-covalent interactions, resulting in a decoupling of material chemical properties from their physical and mechanical properties. Additionally, the reversible self-assembly of these solids can often be carried out under mild conditions in solution. This flexibility facilitates endless possibilities towards material design with applications in, e.g., construction, pharmaceutical manufacturing, separations, catalysis, and organic electronics. For instance, most active pharmaceutical ingredients (APIs) are formulated as crystalline solids. A significant focus of the computational design of crystalline materials is dedicated to predicting a rigorous thermodynamic stability ranking among all possible bulk phases that may result from crystallization. To this aim, increasingly sophisticated approaches have emerged in recent years; these include accurate dispersion models in electronic structure calculations and the introduction of quantum effects to calculate lattice energies; the application of machine learning methods to accurately estimate the thermodynamic stability of crystal polymorphs ; and the development of novel approaches for the calculation of relative lattice free energies .