Rhinovate

About Rhinovate

Rhinovate, legally known as Dingxi Intelligent Innovation (Shenzhen) Technology Co., Ltd., is an AI-driven company specializing in new materials research and development. Founded in 2025 and headquartered in Shenzhen, China, the firm was established with incubation support from Peking University's research programs. The core team comprises talent from prominent institutions such as Peking University, Tsinghua University, University of Cambridge, Stanford University, and MIT. Since its inception, Rhinovate has secured significant capital, completing its third funding round within a year by September 2026, including a Pre-A round of several hundred million yuan led by CDH Baifu. Previous investors include Kinzon Capital, YuanSheng Ventures, Shanghai Future Industry Fund, and XtalPi. The company's mission is to transform the traditionally experience-dependent and prolonged process of materials development into a computable, verifiable, and scalable engineering system. It addresses the challenge of a five-to-ten-year R&D cycle for new materials, where computation, experimentation, and production scale-up often operate in disconnected silos. Rhinovate's core offering is its Physical AI platform, the RhinoAI Intelligent R&D Platform, which integrates an 'AI Scientist' and a 'Self-Driving Lab'. This system creates a closed-loop process encompassing design, execution, characterization, feedback, and optimization. The platform is designed to autonomously iterate models, design material formulations, recommend equipment parameters, plan experimental protocols, and execute the preparation and validation of materials in the physical world. Rhinovate's business model involves providing its Physical AI platform and materials R&D services to clients. The company engages in deep collaboration with industry leaders like Wanhua Chemical, BTR, and JinYu New Energy, and facilitates technology transfer through joint laboratories. Its technology targets the discovery of essential materials for high-growth sectors, including new energy, semiconductors, and aerospace. A key component of its technology stack is the RhinoMat, a foundational large model for materials science. Proceeds from its recent funding are allocated to further develop RhinoMat, build multi-scenario orchestrable AI agents, enhance its autonomous evolution laboratory systems, and scale up the validation of carbon-based high-performance materials.