A Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)-led group of global experts has set out how AI, robotics and lab automation could accelerate discovery across chemistry, biology and materials science, arguing for human-in-the-loop, standards-based “self-driving” laboratories.
Co-authored by researchers from Abu Dhabi’s MBZUAI, the Technical University of Munich, the University of Toronto and others, the article sketches a blueprint for “closed-loop” labs where AI proposes experiments, robots execute them and machine learning analyses results to improve the next run.
The authors highlight six priorities: robust autonomy, digital twins for planning and validation, flexible levels of automation, the use of foundation models and classical ML where appropriate, interoperability standards, and rigorous approaches to reproducibility, safety and ethics.
Key implementation barriers include fragmented hardware/software interfaces and non-standard data. The authors call for FAIR data practices, vendor-neutral protocols (e.g. SiLA, OPC UA), and shared infrastructure to avoid vendor lock-in and lower entry costs for smaller labs.
They also flag the need for “science-as-a-service” models so researchers can access cloud-connected lab capacity remotely, and for workforce upskilling as autonomy scales.
“Automation should amplify scientists, not replace them,” said professor Sami Haddadin, MBZUAI vice president for research, noting that pairing AI planning with robotic execution can shorten discovery cycles while keeping experts in the loop.
For the Middle East and Africa, the paper’s framework aligns with national strategies to build R&D capacity without importing entire facilities: modular, standards-driven cells can be assembled locally; digital twins can cut commissioning time; and remote access can widen participation beyond major hubs.
MBZUAI said the piece stems from a cross-disciplinary workshop held at IEEE ICRA 2024 and aims to catalyse international collaborations on interoperable “science factories”.
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