用可视化工具辅助发现二维半导体，提升小样本下的预测可靠性。
SemiConLens: Visual Analytics for 2D Semiconductor Discovery
- 结合机器学习与人类经验，用新方法处理数据稀疏问题。
- 在有限数据下仍能准确预测并显示材料性能不确定性。
- 适合材料科研人员快速筛选和对比二维半导体候选物。
近年来，学术界和产业界在探索新型二维（2D）半导体材料方面取得显著进展，因其有望缓解传统半导体因硅层过薄导致的性能退化问题。然而，现有方法（如密度泛函理论或基于机器学习的方法）面临数据集小、结果不可靠及可信度不足等挑战。为此，本文提出SemiConLens，一种融合人类专家知识与机器学习能力的可视化分析方法，以实现高效且可靠的2D半导体发现。我们首先开发了新的相关性感知多变量插补（CAMI）方法，并利用自编码器等模型，在数据稀缺条件下更有效地学习并揭示预测不确定性。在此基础上，设计包含三个联动视图的可视化模块，支持研究人员交互式筛选、发现与比较2D半导体候选物。通过创新的圆形符号设计与聚类感知布局优化方法，全面展示用户可配置的关键属性及可能的预测不确定性，确保发现过程的可靠性与可信性。通过定量评估、专家访谈和实际案例验证，结果表明SemiConLens能够有效辅助材料研究人员发现理想的2D半导体材料。
原文摘要 · Abstract (English)
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. Specifically, we first develop a new Correlation Aware Multivariate Imputation (CAMI) method and use ML models like autoencoder, which can better learn from limited data and reveal uncertainty, to address the challenge of sparse data in semiconductivity prediction. Built upon this, our visualization module, consisting of three visualization views with linked interactions, allows material researchers to interactively filter, discover and compare 2D semiconductor candidates. A novel circular glyph design and a new cluster-aware layout optimization approach are proposed to effectively display all the user-configurable key attributes and possible prediction uncertainties of each semiconductor candidate, ensuring a reliable and trustable 2D semiconductor discovery. We assess SemiConLens through quantitative evaluations, expert interviews, and use cases. The results demonstrate SemiConLens's capability to help material researchers conduct effective discovery of desirable 2D semiconductors.
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