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Abstract
This paper proposes MatDesINNe, an inverse design framework utilizing invertible neural networks to map between materials design parameters and target properties. Applied to band gap engineering in 2D MoS2, MatDesINNe generates high-fidelity materials candidates with near-chemical accuracy, offering insights into metal-insulator transitions crucial for neuromorphic applications. The framework's generality allows extension to other materials and properties.
Publisher
npj Computational Materials
Published On
Dec 09, 2021
Authors
Victor Fung, Jiaxin Zhang, Guoxiang Hu, P. Ganesh, Bobby G. Sumpter
Tags
inverse design
invertible neural networks
materials design
band gap engineering
MoS2
metal-insulator transitions
neuromorphic applications

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