Machine learning-assisted high-throughput screening of superlattice-like O-PCM thin films

Non-volatile optical phase change materials with superlattice-like structures (O-PCMs-SLL) offer transformative application potential for photonic integration with low optical loss and enhanced thermal stability. However, the optimization of O-PCMs-SLL is complicated by a high-dimensional design space in which key structural parameters (e.g., modulation period, layer thickness, deposition sequence) exhibit complex interdependencies. Here, by integrating machine learning with high-throughput method, we establish a data-driven framework for rapid screening of O-PCMs-SLL materials, effectively navigating the vast combinatorial design space. A pre-trained model, built on AI-ready high-throughput experimental data, decodes the composition-structure-process-optical performance relationship for Ge-Sb-X (Te, Sn, Se) SLL thin films. Through a dual-phase learning architecture that strategically combines transfer learning and active learning, the pre-trained model can be adaptively navigated to extrapolated domains, accommodating variations in nanoscale periodicity of the SLL structure. This framework achieves an 85% reduction in data requirements for accurate optimization in the extrapolated space, demonstrating a 274-fold efficiency enhancement in discovery rate when benchmarked against conventional trial-and-error approaches. Our work highlights the pivotal role of AI-informed guidance within high-throughput experimental data-driven materials discovery and provides a generalizable blueprint for accelerated exploration of complex multi-component functional materials.