Inverse design of two-dimensional materials with invertible neural networks

Understand

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.