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

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Darvish, K ORCID: 0000-0003-4984-432X, Sohal, A, Mandal, A, Fakhruldeen, H, Radulov, N, Zhou, Z ORCID: 0000-0001-9478-9361, Veeramani, S ORCID: 0000-0003-2538-0022, Choi, J, Han, S, Zhang, B et al (show 13 more authors) , Chae, J, Wright, A ORCID: 0009-0008-7068-0482, Wang, Y ORCID: 0009-0009-1848-1555, Darvish, H ORCID: 0000-0001-7224-4627, Zhao, Y, Tom, G, Hao, H ORCID: 0000-0002-0176-3929, Bogdanovic, M, Pizzuto, G ORCID: 0000-0002-8541-0759, Cooper, AI ORCID: 0000-0003-0201-1021, Aspuru Guzik, A ORCID: 0000-0002-8277-4434, Shkurti, F and Garg, A ORCID: 0000-0003-0482-4296 (2026) MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation Nature Computational Science, 6 (1). pp. 67-82. ISSN 2662-8457, 2662-8457

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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.

Item Type:	Article
Uncontrolled Keywords:	46 Information and Computing Sciences, 4607 Graphics, Augmented Reality and Games, Bioengineering, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Computer Science & Informatics Faculty of Science & Engineering > School of Computer Science & Informatics > Artificial Intelligence Faculty of Science & Engineering > School of Physical Sciences Faculty of Science & Engineering > School of Physical Sciences > Chemistry
Depositing User:	Symplectic Admin
Date Deposited:	06 Jan 2026 11:22
Last Modified:	16 Mar 2026 12:28
DOI:	10.1038/s43588-025-00924-4
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3196402
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