From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning

作者：Xinyu Chen, Shuaihua Lu, Qian Chen, Qionghua Zhou, Jinlan Wang · 发表于：Nature Communications · 年份：2024 · DOI：10.1038/s41467-024-49686-z · 被引用次数：65 · 研究领域：Machine Learning in Materials Science、Electronic and Structural Properties of Oxides、Ferroelectric and Negative Capacitance Devices

Data scarcity is one of the critical bottlenecks to utilizing machine learning in material discovery. Transfer learning can use existing big data to assist property prediction on small data sets, but the premise is that there must be a strong correlation between large and small data sets. To extend its applicability in scenarios with different properties and materials, here we develop a hybrid framework combining adversarial transfer learning and expert knowledge, which enables the direct prediction of carrier mobility of two-dimensional (2D) materials using the knowledge learned from bulk effective mass. Specifically, adversarial training ensures that only common knowledge between bulk and 2D materials is extracted while expert knowledge is incorporated to further improve the prediction accuracy and generalizability. Successfully, 2D carrier mobilities are predicted with the accuracy over 90% from only crystal structure, and 21 2D semiconductors with carrier mobilities far exceeding silicon and suitable bandgap are successfully screened out. This work enables transfer learning in simultaneous cross-property and cross-material scenarios, providing an effective tool to predict intricate material properties with limited data.