SemiConLens: Visual Analytics for 2D Semiconductor Discovery
The past few years have witnessed vibrant efforts in discovering new two-dimensional (2D) semiconductor materials from both academia and the industry, due to their promising potential in resolving the severe performance deterioration of traditional semiconductors resulting from condensed silicon thickness. However, existing methods (e.g., Density Functional Theory (DFT) or machine-learning-based approaches) suffer from various challenges such as small datasets, and reliability and trustworthiness issues. To bridge this gap, we propose SemiConLens, a visual analytics approach to combine human expertise with the power of ML to enable effective and reliable 2D semiconductor discovery. Specif...
| Authors | Kavinda Athapaththu, Shiwei Chen, Yuan Fang et al. |
|---|---|
| Primary category | cs.HC |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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