Continuous discovery of novel 2D materials via dual active learning-driven generative models

Abstract Generative artificial intelligence is transforming materials discovery by creating unexplored candidates. However, such models are trapped in historical data bias, particularly for data-scarce systems like two-dimensional (2D) materials, leading to repetitive outputs rather than genuine discoveries. Here, we introduce DuALGen, a dual active learning framework that mitigates these limitations. DuALGen couples two complementary loops to enrich data diversity and correct data bias: a generative loop that uses dynamic, multi-criteria sampling to drive exploration of the design space, and a predictive loop that samples outlier to counter distribution shift, enabling reliable evaluation of novel, previously unknown candidates. Applied to 2D materials, DuALGen uncovers more than 10,000 stable, distinct compounds, including thousands of high-performance candidates for electronic applications. This self-updating workflow connects generative models to uncharted chemical spaces, and offers a practical route to continuous discovery of new materials.