Rossi, KevinCumby, James2020-01-082020-01-082020-01-082019-12-2710.1002/qua.26151https://infoscience.epfl.ch/handle/20.500.14299/164401WOS:000504507600001Establishing a unified framework for describing the structures of molecular and periodic systems is a long-standing challenge in physics, chemistry, and material science. With the rise of machine learning methods in these fields, there is a growing need for such a method. This perspective aims to discuss the development and use of three promising approaches-topological, atom-density, and symmetry-based-for the prediction and rationalization of physical, chemical, and mechanical properties of atomistic systems across different scales and compositions.Chemistry, PhysicalMathematics, Interdisciplinary ApplicationsQuantum Science & TechnologyPhysics, Atomic, Molecular & ChemicalChemistryMathematicsPhysicsatom densityconnectivitydata drivendescriptorsmachine learningsymmetry distortionsframeworktext::journal::journal article::research article