A first principles informed machine learning model for electronic structure prediction of strained 1D nanomaterials

Quasi-one-dimensional (1D) nanomaterials are an important class of low-dimensional matter which include nanotubes of arbitrary chirality, nanowires, nanosprings, nanoribbons, and nanoassemblies. The fascinating electronic, optical, transport and magnetic properties of these materials offer unparalleled opportunities for impacting the design of novel quantum, photonic and electromagnetic devices. We present a first-principles informed machine learning model that can predict the electronic structure of such materials in their natural or distorted states, while they are being subjected to deformation modes such as torsion and extension/compression. Our model includes global structural symmetries, atomic relaxation effects and uses a symmetry-adapted version of Kohn-Sham Density Functional Theory to generate the input data. We use armchair single wall carbon nanotubes as a prototypical example, and demonstrate the use of the model to predict various electronic fields when the radius of the nanotube, its axial stretch, and the twist per unit length are specified as inputs. Our model is likely to find applications in areas where the interplay of strain and electronic properties at the nanoscale (i.e., strain engineering) plays an important role.