  The ability to readily design novel materials with chosen functional
properties on-demand represents a next frontier in materials discovery.
However, thoroughly and efficiently sampling the entire design space in a
computationally tractable manner remains a highly challenging task. To tackle
this problem, we propose an inverse design framework (MatDesINNe) utilizing
invertible neural networks which can map both forward and reverse processes
between the design space and target property. This approach can be used to
generate materials candidates for a designated property, thereby satisfying the
highly sought-after goal of inverse design. We then apply this framework to the
task of band gap engineering in two-dimensional materials, starting with MoS2.
Within the design space encompassing six degrees of freedom in applied tensile,
compressive and shear strain plus an external electric field, we show the
framework can generate novel, high fidelity, and diverse candidates with
near-chemical accuracy. We extend this generative capability further to provide
insights regarding metal-insulator transition, important for memristive
neuromorphic applications among others, in MoS2 which is not otherwise possible
with brute force screening. This approach is general and can be directly
extended to other materials and their corresponding design spaces and target
properties.

Materials Science;Machine Learning