MatteriX

Accelerated materials discovery is critical for addressing global challenges. However, developing new laboratory workflows relies heavily on real-world experimental trials, and this can hinder scalability because of the need for numerous physical make-and-test iterations. Here, we present MatteriX, a multi-scale, GPU-accelerated robotic simulation framework designed to create high-fidelity digital twins of chemistry labs, thus accelerating workflow development. This multi-scale digital twin simulates robotic physical manipulation, powder and liquid dynamics, device functionalities, heat transfer, and basic chemical reaction kinetics. This is 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. Our approach demonstrates sim-to-real transfer in robotic chemistry setups, reducing reliance on costly real-world experiments and enabling the testing of hypothetical automated workflows in silico.