3D Scattering Transform Representation of Materials: From Molecules to Crystals

Computational materials scientists have generated huge databases of quantum accurate calculations for materials and their properties. These large databases have been mined to find correlations between structure and macroscopic properties in order to find new interesting materials. Machine learning potentials are gradually replacing more expensive first-principles calculations as surrogate models to obtain information on stability and properties of a material based on the microscopic arrangement of its atoms. In order for these machine learning potentials to be effective, the representations must be invariant to permutation, isometric transformations, and stable to deformation. In this talk, we present a 3D scattering transform representation that satisfies these properties. We apply this representation to molecules and crystal systems to show the robustness of this representation.