Dingxi Intelligence completed an angel round of financing worth tens of millions of yuan. It uses AI to reconstruct the paradigm of materials R & D and kickstarts the era of "intelligent manufacturing" of new materials.
The AI-driven new material design and intelligent R & D enterprise, Rhinovate™, announced the completion of tens of millions of yuan in angel round financing, jointly led by Kunzhong Capital and Yuansheng Venture Capital. The funds from this round of financing will be mainly used for team expansion, core algorithm R & D, and product implementation.
The company is building the RhinoWise™ intelligent material innovation platform. With the AI + new material closed-loop architecture of "design - simulation - preparation - characterization", it aims to achieve a leapfrog improvement in material R & D efficiency from several years to several months.
Rhinovate was founded in July 2025. The project team was incubated at the Shenzhen-Hong Kong Hetao Science and Technology Innovation Center of Peking University Shenzhen Graduate School. It consists of top domestic material scientists, material computing, and artificial intelligence experts, with rich experience in scientific research and application practice. It is one of the few top teams in China with full-stack R & D capabilities in "experiment - theory - model - design - process" and commercial service experience. The team is committed to building a new generation of intelligent R & D infrastructure platform for AI for Materials, integrating full-stack technologies such as high-throughput experimental characterization, intelligent spectroscopy analysis, large model prediction, structural performance analysis, and autonomous experimental design and large-scale preparation. From data to application, and from experiment to design and production, it provides a full-life-cycle solution for new material R & D.
Currently, AI is reshaping the paradigm of scientific R & D. Among them, new materials are one of the most promising application areas for industrialization. The discovery, design, and verification of new materials highly rely on multi-modal complex data and cross-scale experimental systems, which are extremely suitable for the learning and optimization characteristics of AI models. The global AI for Science field is moving from algorithm breakthroughs to the platform integration stage of "model + data + experiment", and materials science is at the forefront of this integration. Google DeepMind released a new AI tool, GNoMe, which successfully predicted 2.2 million crystal structures, revolutionizing the field of materials science. The latest diffusion model, MatterGen, developed by the Microsoft team, is specifically designed to generate novel and stable materials, greatly improving the speed of designing materials with desired properties. According to predictions by several international institutions, AI-driven material innovation will release trillions of dollars in production dividends for the global manufacturing industry before 2030.
Based on the self-developed RhinoWise™ driven by a large material model agent, Rhinovate has made a qualitative leap in the ability to understand and predict chemical data and context compared to other large models. Through the GenAI-driven RhinoWise™ automated experimental platform, it can autonomously complete the entire process of materials from "design - simulation - preparation - characterization", achieving super automation in scenarios such as scientific research experiments and production operations. AI models and tools accelerate material innovation and industrialization in areas such as aerospace composite materials and semiconductor materials, forming a sustainable and iterative material R & D ecosystem, and bringing material design into an intelligent era of computability, predictability, and iterability.
Currently, Rhinovate's product services will cooperate with leading enterprises in the domestic chemical and new energy fields to form a one-stop service capability of "Solution + Tools + Services" for actual enterprise application scenarios, implement benchmark applications for AI for Science, and quickly explore a viable business model.
Lu Haifeng, CEO of Rhinovate, said that the success of this round of financing is due to the market's double recognition of the AI + materials track and the Rhinovate team. With the development of AI, the innovation logic of materials science is also changing: future breakthroughs will not only depend on the scale of experiments or computing power, but more importantly, whether we can raise truly valuable questions, efficiently verify solutions in the real world, and ultimately build a sustainable and reproducible R & D closed-loop and innovation ecosystem. Rhinovate is such a team deeply involved in the intersection of materials science and AI technology, committed to deeply integrating data-driven research methods with industry knowledge and promoting the transformation of the new material R & D paradigm in the AI era.
About this round of financing
Wang Jun, the founding partner of Kunzhong Capital, said:
Materials science is undergoing an AI-driven paradigm revolution. From Google's GNoME to Microsoft's MatterGen, global technology giants are accelerating their layout in this trillion-dollar track. The traditional material R & D cycle of 10 - 20 years has become the biggest bottleneck restricting industrial innovation. The Rhinovate team has the "dual-core" advantage of both materials science foundation and AI technology strength. The AI closed-loop platform of "design - simulation - preparation - characterization" it has built is not a simple combination of technologies, but a systematic solution starting from industrial pain points. From new energy batteries to semiconductor materials, they are compressing the material R & D cycle from several years to several months. Kunzhong Capital has long been concerned about hard technology innovation that can reshape industrial efficiency. Relying on the innovation ecosystem of Peking University and the Shenzhen-Hong Kong Hetao area, Rhinovate has achieved efficient connection between academic frontiers and industrial needs. We believe that Rhinovate has the potential to become a leading enterprise in the AI + new materials field in China.
Liu Xiao, the managing director of Yuansheng Venture Capital, said:
AI for Science is at the critical point where the scientific research paradigm and industrial paradigm meet. We believe that the value of AI in the field of materials science will first achieve a leap from a scientific research tool to an industrial infrastructure. Rhinovate has built a new R & D closed-loop at the three levels of algorithm, experiment, and data through an interdisciplinary and systematic approach. Its practice in the fields of carbon-based materials and high-throughput experiments provides a Chinese sample for AI-driven material innovation. We are optimistic about the execution ability of the Rhinovate team in both scientific research implementation and industrial collaboration, and we also believe that the company will accelerate R & D innovation and achieve win-win value in cooperation with leading material enterprises.