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However, existing methods (e.g., Density Functional Theory (DFT) or machine\u2010learning\u2010based 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\u2010aware layout optimization approach are proposed to effectively display all the user\u2010configurable 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.<\/jats:p>","DOI":"10.1111\/cgf.70455","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T10:55:26Z","timestamp":1781175326000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SemiConLens: Visual Analytics for 2D Semiconductor Discovery"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8641-7768","authenticated-orcid":false,"given":"Kavinda","family":"Athapaththu","sequence":"first","affiliation":[{"name":"Nanyang Technological University"},{"name":"Singapore Management University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9106-7989","authenticated-orcid":false,"given":"Shiwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4265-5289","authenticated-orcid":false,"given":"Yuan","family":"Fang","sequence":"additional","affiliation":[{"name":"Singapore Management University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3829-0010","authenticated-orcid":false,"given":"Sanchali","family":"Mitra","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1637-1610","authenticated-orcid":false,"given":"Yee Sin","family":"Ang","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0092-0793","authenticated-orcid":false,"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"},{"name":"Singapore Management University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21165480"},{"key":"e_1_2_11_3_2","first-page":"1","volume-title":"2024 IEEE International Workshop on Information Forensics and Security (WIFS)","author":"Bindini L.","year":"2024"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0337-2"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/235815.235821"},{"key":"e_1_2_11_6_2","unstructured":"BorgoR. 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