Dingxi Zhichuang (Rhinovate) has completed a Pre-A funding round of several hundred million yuan, led by Dinghui Baifu with participation from multiple investors. The funds will be used to develop its materials science foundation model and autonomous laboratory systems, aiming to integrate AI scientists with physical experiments to speed up materials discovery and validation.
SynthePulse Insight · AI deep readingMembers
Version 1 · 1 source
AI new materials company Rhinovate™ has completed a Pre-A funding round of hundreds of millions of yuan, with its core focus on closing the 'model-experiment-scale-up' loop, enabling AI Scientists to not only generate candidate materials but also complete real-world validation.
Rhinovate™ recently completed a Pre-A funding round of hundreds of millions of yuan, led by DCP Capital, with participation from Jiuhe Venture Capital, Binfu Capital, Loongson Venture Capital, Shunxi Fund, and Zhongguancun Capital. Previously, it had received investments from Kunchuang Capital, Yuansheng Venture Capital, Shanghai Future Industry Fund, and XtalPi, marking its third funding round within a year.
The funds will be primarily used for the R&D of the RhinoMat materials science foundational large model and underlying technologies, the construction of multi-scenario orchestrated AI agents and autonomous evolution laboratory systems, and the scale-up validation of carbon-based high-performance materials.
Founded in 2025 and incubated with the support of Peking University's scientific research strength, the core team hails from Peking University, Tsinghua University, University of Cambridge, Stanford University, and MIT. Its technical approach emphasizes being closer to the physical world, integrating AI Scientist, autonomous experimental systems, and pilot-scale capabilities through the RhinoAI platform.
Rhinovate™ adopts cross-scale material compilation as a unified framework, incorporating material structure, formulation, process, and performance into a single computation-experiment chain. Its self-developed Rhino Physical AI platform is driven by the collaboration of AI Scientist and Self-Driving Lab: the former is responsible for understanding research goals, invoking knowledge and material models, generating research plans, and orchestrating tasks; the latter completes preparation, testing, and data feedback through robots, intelligent experimental workstations, and high-throughput characterization equipment.
The system decomposes R&D into executable, feedback-driven task flows. AI Scientist proposes hypotheses and strategies, robots execute high-throughput experiments, and scientists define goals, interpret mechanisms, and make engineering decisions. Together, they operate within a closed loop of 'design-execute-characterize-feedback-optimize'. After each round of experiments, the system gains new data and evidence, updating the next round of decisions.
At the model level, Rhino is building a polymer-focused model, RhinoMat, based on three-dimensional equivariant graph neural networks and long-range interaction modules, providing a unified representation of polymer structures. The model can construct force fields to simulate polymer systems, use diffusion models to inversely generate target polymers, and integrate structural representations into surrogate models for Bayesian optimization, continuously calibrated by experimental feedback.
This article's information primarily originates from a report by Synced on Rhinovate™, which is a secondary source. Specific figures on funding, data, and cases are as disclosed by the company or reported by media and have not been independently verified. Expressions such as 'strongest in the industry' for performance metrics are company claims and should be treated with caution.
Primary report
Primary source