smol: A Python package for cluster expansions andbeyond

The growing research focus on multi-principal element materials-spanning a variety of applications, such as electrochemical (Lun et al., 2020), structural (George et al., 2019), semiconductor, thermoelectric, magnetic, and superconducting (Gao et al., 2018) materials-necessitates the development of computational methodology capable of resolving details of atomic configuration and resulting thermodynamic properties.The cluster expansion (CE) method is a formal and effective way to construct functions of atomic configuration by coarse-graining materials properties, such as formation energies, in terms of species occupancy lattice models (Sanchez et al., 1984).The cluster expansion method coupled with Monte Carlo sampling (CE-MC) is an established and effective way to resolve atomic details underlying important thermodynamic properties (Van der Ven et al., 2018).