Achieving efficient inverse design of low-dimensional heterostructures based on a vigorous scalable multi-task learning network.

A scalable multi-task learning (SMTL) model is proposed for the efficient inverse design of low-dimensional heterostructures and the prediction of their optical response. Specifically, several types of nanostructures, including single and periodic graphene-Si heterostructures consisting of n×n graphene squares (n=1∼9), 1D periodic graphene ribbons, 2D arrays of graphene squares, pure Si cubes and their periodic array counterparts, are investigated using both traditional finite element method and SMTL network, with the former providing training data (optical absorption) for the latter. There are two important algorithms implemented in SMTL model: one is the normalization mechanism that makes different parameters of different structures on the same scale, ensuring that SMTL network can deal with tasks with different dataset impartially and without bias; the other one is used to capture the impact of nanostructures’ dimensions on their optical absorption and thus improve the generalization ability of SMTL. Utilizing SMTL model, we first study the absorption property of the multiple shaped nanostructures and look deeper into the impacts of n×n graphene squares and Si cuboid on the optical absorption of their heterostructures. Equally important, the multi-structure inverse design functionality of SMTL is confirmed in this context, which not only owns high accuracy, fast computational speed, and excellent generalizable ability, but also can be applied to contrive new structures with desired optical response. This work adds to the rapidly expanding field of inverse design in nanophotonics and establishes a multi-task learning framework for heterostructures and more complicated nanoparticles.