MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation

TL;DR

MATTERIX introduces a GPU-accelerated digital twin framework for robotics-assisted chemistry labs, enabling high-fidelity simulation of workflows to accelerate materials discovery and reduce real-world experimental reliance.

Contribution

This work presents a multiscale, high-fidelity digital twin platform integrating physics, chemistry, and robotics, with open-source tools for flexible workflow design and sim-to-real transfer.

Findings

Successful simulation of chemical reactions and robotic manipulations

Reduced need for physical experiments in workflow testing

Enabling hypothetical workflow testing in silico

Abstract

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 multiscale, graphics processing unit-accelerated robotic simulation framework designed to create high-fidelity digital twins of chemistry laboratories, thus accelerating workflow development. This multiscale 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 graphics processing unit-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. The project website is available at https://accelerationconsortium.github.io/Matterix/ .