First principles and data driven simulations of chiral matter

Chiral matter, i.e., structures with non-superimposable mirror images, offers a great many opportunities for impacting the design of novel electromagnetic, photonic and quantum hardware devices. Chiral matter includes nanostructures with helical and cyclic symmetries and includes quasi-one-dimensional materials such as nanotubes, nanowires, nanoribbons and nanocoils. In this talk, I will outline a set of first principles and data driven approaches for the study of such materials, In particular, I will describe a symmetry adapted real space formulation of Kohn-Sham theory and a machine learning model that is trained on data from such specialized symmetry adapted calculations. I will describe applications of these tools to investigate the electromechanical response of group IV nanotubes under torsional and axial strains, as well as the use of these tools to discover novel phases of chiral matter with strongly correlated electronic states.