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/ .

翻译：加速材料发现对于应对全球性挑战至关重要。然而，新实验室工作流程的开发严重依赖于现实世界的实验验证，这可能会因需要大量物理制造与测试迭代而阻碍可扩展性。本文介绍MATTERIX，一个多尺度、图形处理器加速的机器人仿真框架，旨在创建高保真度的化学实验室数字孪生，从而加速工作流程开发。该多尺度数字孪生模拟机器人物理操控、粉末与液体动力学、设备功能、热传递及基本化学反应动力学。这是通过将逼真的物理仿真与照片级真实感渲染，与模块化图形处理器加速语义引擎相结合实现的，该引擎对逻辑状态和连续行为进行建模，以在不同抽象层级上模拟化学工作流程。MATTERIX通过开源资产库和接口简化了数字孪生环境的创建，同时支持通过分层计划定义和包含基于学习方法的模块化技能库进行灵活的工作流程设计。我们的方法在机器人化学实验装置中展示了仿真到现实的迁移能力，减少了对昂贵现实实验的依赖，并支持在计算机中测试假设的自动化工作流程。项目网站位于 https://accelerationconsortium.github.io/Matterix/ 。