MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911386477527040 |
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| author | Darvish, Kourosh Sohal, Arjun Mandal, Abhijoy Fakhruldeen, Hatem Radulov, Nikola Zhou, Zhengxue Veeramani, Satheeshkumar Choi, Joshua Han, Sijie Zhang, Brayden Chae, Jeeyeoun Wright, Alex Wang, Yijie Darvish, Hossein Zhao, Yuchi Tom, Gary Hao, Han Bogdanovic, Miroslav Pizzuto, Gabriella Cooper, Andrew I. Aspuru-Guzik, Alán Shkurti, Florian Garg, Animesh |
| author_facet | Darvish, Kourosh Sohal, Arjun Mandal, Abhijoy Fakhruldeen, Hatem Radulov, Nikola Zhou, Zhengxue Veeramani, Satheeshkumar Choi, Joshua Han, Sijie Zhang, Brayden Chae, Jeeyeoun Wright, Alex Wang, Yijie Darvish, Hossein Zhao, Yuchi Tom, Gary Hao, Han Bogdanovic, Miroslav Pizzuto, Gabriella Cooper, Andrew I. Aspuru-Guzik, Alán Shkurti, Florian Garg, Animesh |
| contents | 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/ . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13232 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation Darvish, Kourosh Sohal, Arjun Mandal, Abhijoy Fakhruldeen, Hatem Radulov, Nikola Zhou, Zhengxue Veeramani, Satheeshkumar Choi, Joshua Han, Sijie Zhang, Brayden Chae, Jeeyeoun Wright, Alex Wang, Yijie Darvish, Hossein Zhao, Yuchi Tom, Gary Hao, Han Bogdanovic, Miroslav Pizzuto, Gabriella Cooper, Andrew I. Aspuru-Guzik, Alán Shkurti, Florian Garg, Animesh Robotics 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/ . |
| title | MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.13232 |