2D Janus Halogenated Silicene database

Recent advances in theoretical computing and machine learning (ML) have facilitated the in-depth exploration of two-dimensional (2D) materials. We combines ML with 2D materials screening with the aim of exploring novel halogenated silicene-based materials for applications in photocatalysis and solar cells. We construct a data-driven framework that combines first-principles computation and self-contained databases for supervised learning to develop highly accurate predictive models. This dataset containing 286 halogenated silicene structures, which is 11.5% of the total database, is obtained after Density-functional theory (DFT) calculations.理论计算与机器学习（Machine Learning，ML）领域的新近进展，推动了二维（2D）材料的深入探索。本研究将机器学习与二维材料筛选相结合，旨在探索可应用于光催化与太阳能电池的新型卤化硅烯基材料。本研究构建了一套数据驱动的研究框架，该框架结合第一性原理计算与自建数据库，依托监督学习开发高精度预测模型。本数据集包含286个卤化硅烯结构，占总数据库的11.5%，其数据经密度泛函理论（Density-functional theory，DFT）计算获得。