Struct-R1: Material Structure Search with Multi-Fidelity Measurement in Agentic Loops

Materials discovery requires a research loop in large structure space with multi-tier measurements with different fidelity and costs, from machine-learned surrogates to DFT calculations and experiments. Manual exploration can be time-consuming, while statistical search methods and existing agent designs struggle to achieve high search efficiency with trade-off between fidelity and cost. We present Struct-R1, a self-evolving agentic framework with two coupled loops. The inner search loop performs physics-grounded reasoning and perturbation proposal, and manages cross-tier relationships to improve search efficiency. The outer evolution loop reflects on individual search steps and converts their feedback into reusable knowledge. Through the two loops, Struct-R1 progressively refines both where to explore and how to allocate search budget on the measurements. We evaluate Struct-R1 on representative material-property search tasks, such as Raman and phonon properties, and further validate it using real research data for well-studied materials to assess its effectiveness in realistic scientific settings.