OneMat: Generative Optimization from Scientific Literature for Materials Design

Materials design still relies heavily on costly physical experimentation and search over predefined candidate spaces, limiting both the efficiency and breadth of exploration. Recent generative approaches in materials science have advanced candidate-level design by proposing novel compositions and atomic structures, but executable synthesis procedures for their realization remain largely outside iterative generation and optimization. We introduce OneMat, a literature-driven framework for synthesis-oriented generative optimization, where LLMs generate and iteratively refine executable synthesis plans. To enable this paradigm, OneMat transforms scientific literature into a structured domain resource containing evidence-grounded synthesis knowledge, generation examples, and preference signals, and integrates generation with retrieval and evaluation in an iterative optimization loop. We evaluate OneMat on acidic oxygen evolution reaction (OER) catalyst design using a literature corpus of 5,642 papers across mining, synthesis-plan generation, optimization, and wet-lab validation. Evidence-first mining consistently reduces unsupported extraction across four LLMs, while literature-derived post-training improves synthesis-plan quality, increasing the evaluator score from 0.51 (Base) to 0.62 (SFT) and 0.69 (DPO), and enhancing closed-loop optimization. Finally, automated wet-lab experiments translate OneMat-generated synthesis plans into functional catalysts, with the best-performing design discovered through limited experimental exploration achieving an overpotential of 155 mV at 10 mA cm${}^{-2}$ and only a 21 mV increase after 500 CV cycles. Together, these results establish a route from scientific literature to experimentally validated synthesis-oriented generative optimization for materials design.