Publication date: 15th December 2025
Accelerated materials discovery is critical for addressing global challenges. However, developing new laboratory workflows still relies heavily on real-world experimental trials, which limits scalability due to the need for numerous physical make-and-test iterations. This talk presents Matterix, a multi-scale, GPU-accelerated robotic simulation framework designed to create high-fidelity digital twins of chemistry labs. This digital twin simulates robotic physical manipulation, powder and liquid dynamics, device functionalities, heat transfer, and basic chemical reaction kinetics. These capabilities are enabled by integrating realistic physics simulation and photorealistic rendering with a modular GPU-accelerated semantics engine, which models logical states and continuous behaviors to simulate chemistry workflows across different levels of abstraction. Matterix streamlines the creation of digital-twin environments through open-source asset libraries and interfaces, while enabling flexible workflow design via hierarchical plan definition and a modular skill library that incorporates learning-based methods. This approach enables sim-to-real transfer in robotic chemistry setups, reducing reliance on costly real-world experiments and allowing hypothetical automated workflows to be tested entirely in silico.
