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Automated synthesis of oxygen-producing catalysts from Martian meteorites by a robotic AI chemist


Living on Mars requires the ability to synthesize chemicals that are essential for survival, such as oxygen, from local Martian resources. However, this is a challenging task. Here we demonstrate a robotic artificial-intelligence chemist for automated synthesis and intelligent optimization of catalysts for the oxygen evolution reaction from Martian meteorites. The entire process, including Martian ore pretreatment, catalyst synthesis, characterization, testing and, most importantly, the search for the optimal catalyst formula, is performed without human intervention. Using a machine-learning model derived from both first-principles data and experimental measurements, this method automatically and rapidly identifies the optimal catalyst formula from more than three million possible compositions. The synthesized catalyst operates at a current density of 10 mA cm−2 for over 550,000 s of operation with an overpotential of 445.1 mV, demonstrating the feasibility of the artificial-intelligence chemist in the automated synthesis of chemicals and materials for Mars exploration.

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Fig. 1: Workflow of an all-encompassing system for the on-site design and production of an OER electrocatalyst on Mars by an AI chemist consisting of a mobile robot, a computational ‘brain’, a cloud server and 14 task-specific workstations.
Fig. 2: Theoretical simulation and performance prediction of multimetallic hydroxides.
Fig. 3: Searching for the best OER catalyst from Martian meteorites conducted by AI chemist.
Fig. 4: AI chemist completes the electrochemical measurement and evaluation of practical application potential of the catalyst derived from Martian meteorites.

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Data availability

The data that support the findings of this study are available in the paper, its Supplementary Information and Supplementary Video 1.

Code availability

The code used for training an NN model for OER prediction with theoretical data and robot-driven experimental data is available on GitHub at


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Y.L. acknowledges funding support for this research from the Innovation Program for Quantum Science and Technology (Grant 2021ZD0303303). J.J. gratefully acknowledges financial support by the National Natural Science Foundation of China (Grants 22025304, 22033007) and the CAS Project for Young Scientists in Basic Research (Grant YSBR-005). Q.Z. gratefully acknowledges the financial support of the National Natural Science Foundation of China (Grant 22103076) and Anhui Provincial Natural Science Foundation (Grant 2108085QB63). We also gratefully acknowledge the USTC Center for Micro- and Nanoscale Research and Fabrication for providing experimental resources and the USTC supercomputing centre for providing computational resources.

Author information

Authors and Affiliations



These authors contributed equally: Q.Z., Y.H., D.Z., L.Z. Q.Z. planned and conducted the robotic experiments and collected and analysed the experiment data. Y.H., D.Z., L.Z. and H.L. performed theoretical simulations and ML training. L.G., R.Y., Z.S. and M.L. assisted with the spectroscopic characterization and data analysis. H.X., B.Z. and J.C. were responsible for writing test scripts. X.T. and Y.Z. contributed to the development of robotic operation module, robotic arm motion planning and force control. J.Z. and B.C. helped with the robot platform communication, SLAM, platform motion planning and navigation. T.S. planned robot movement and operation task management system. X.L. and S.C. managed the scheduling optimization of robot experimental tasks at various workstations. X.Z. developed the robotic visual localization algorithm. F.Z. and W.S. designed the entire robot system. G.Y. and W.Z. worked on non-standardized equipment development. S.W., G.Z. and H.Z. contributed to the original draft preparation. L.-L.L. and Z.Z. assisted in the design and execution of experiments under simulated Martian environments. J.J. and Y.L. conceptualized the study, developed the methodology and conducted the investigation and wrote, reviewed and edited the paper. All authors participated in discussions and revisions and provided comments on the paper.

Corresponding authors

Correspondence to Weiwei Shang, Jun Jiang or Yi Luo.

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The authors declare no competing interests.

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Peer review information

Nature Synthesis thanks Leroy Cronin, Zhigang Zou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Peter Seavill, in collaboration with the Nature Synthesis team.

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Supplementary information

Supplementary Information

Experimental details, Supplementary Figs. 1–22, Note 1 and Tables 1–4.

Supplementary Video 1

This video showcases the capabilities of the AI chemist in synthesizing and optimizing oxygen-producing catalysts from Martian meteorites. The process involves automated analysis of Martian ore, catalyst synthesis, characterization, intelligent computing and OER performance testing, which highlights the integration of robotics and AI for complex materials design and manufacture under challenging circumstances.

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Zhu, Q., Huang, Y., Zhou, D. et al. Automated synthesis of oxygen-producing catalysts from Martian meteorites by a robotic AI chemist. Nat. Synth 3, 319–328 (2024).

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