Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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


  1. Gayen, P., Sankarasubramanian, S. & Ramani, V. K. Fuel and oxygen harvesting from Martian regolithic brine. Proc. Natl Acad. Sci. USA 117, 31685–31689 (2020).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  2. Hoffman, J. A. et al. Mars Oxygen ISRU Experiment (MOXIE)—preparing for human Mars exploration. Sci. Adv. 8, eabp8636 (2022).

  3. Wade, J., Dyck, B., Palin, R. M., Moore, J. D. P. & Smye, A. J. The divergent fates of primitive hydrospheric water on Earth and Mars. Nature 552, 391–394 (2017).

    Article  CAS  PubMed  ADS  Google Scholar 

  4. Orosei, R. et al. Radar evidence of subglacial liquid water on Mars. Science 361, 490–493 (2018).

    Article  CAS  PubMed  ADS  Google Scholar 

  5. Kruyer, N. S., Realff, M. J., Sun, W., Genzale, C. L. & Peralta-Yahya, P. Designing the bioproduction of Martian rocket propellant via a biotechnology-enabled in situ resource utilization strategy. Nat. Commun. 12, 6166 (2021).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  6. Yao, Y. et al. Extraterrestrial photosynthesis by Chang’E-5 lunar soil. Joule 6, 1008–1014 (2022).

    Article  CAS  Google Scholar 

  7. Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).

    Article  CAS  PubMed  ADS  Google Scholar 

  8. Rohrbach, S. et al. Digitization and validation of a chemical synthesis literature database in the ChemPU. Science 377, 172–180 (2022).

    Article  CAS  PubMed  ADS  Google Scholar 

  9. Zhu, Q. et al. An all-round AI-chemist with a scientific mind. Natl Sci. Rev. 9, nwac190 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Pyzer-Knapp, E. O., Chen, L., Day, G. M. & Cooper, A. I. Accelerating computational discovery of porous solids through improved navigation of energy-structure-function maps. Sci. Adv. 7, eabi4763 (2022).

    Article  ADS  Google Scholar 

  11. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).

    Article  CAS  PubMed  ADS  Google Scholar 

  12. Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360–365 (2018).

    Article  CAS  PubMed  ADS  Google Scholar 

  13. Bai, L., Hsu, C.-S., Alexander, D. T. L., Chen, H. M. & Hu, X. Double-atom catalysts as a molecular platform for heterogeneous oxygen evolution electrocatalysis. Nat. Energy 6, 1054–1066 (2021).

    Article  CAS  ADS  Google Scholar 

  14. Lin, C. et al. In-situ reconstructed Ru atom array on α-MnO2 with enhanced performance for acidic water oxidation. Nat. Catal. 4, 1012–1023 (2021).

    Article  CAS  ADS  Google Scholar 

  15. Xu, H., Cheng, D., Cao, D. & Zeng, X. C. A universal principle for a rational design of single-atom electrocatalysts. Nat. Catal. 1, 339–348 (2018).

    Article  CAS  Google Scholar 

  16. Craig, M. J. et al. Universal scaling relations for the rational design of molecular water oxidation catalysts with near-zero overpotential. Nat. Commun. 10, 4993 (2019).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  17. Tao, L. et al. Charge transfer modulated activity of carbon-based electrocatalysts. Adv. Energy Mater. 10, 1901227 (2020).

    Article  CAS  Google Scholar 

  18. Walter, M. G. et al. Solar water splitting cells. Chem. Rev. 110, 6446–6473 (2010).

    Article  CAS  PubMed  Google Scholar 

  19. Appelbaum, J. & Flood, D. J. Solar radiation on Mars. Sol. Energy 45, 353–363 (1990).

    Article  ADS  Google Scholar 

  20. McCrory, C. C. L., Jung, S., Peters, J. C. & Jaramillo, T. F. Benchmarking heterogeneous electrocatalysts for the oxygen evolution reaction. J. Am. Chem. Soc. 135, 16977–16987 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Hecht, M. H. et al. Detection of perchlorate and the soluble chemistry of Martian soil at the Phoenix lander site. Science 325, 64–67 (2009).

    Article  CAS  PubMed  ADS  Google Scholar 

  22. Cull, S. C. et al. Concentrated perchlorate at the Mars Phoenix landing site: evidence for thin film liquid water on Mars. Geophys. Res. Lett. 37, L22203 (2010).

  23. Schröder, C. et al. Meteorites on Mars observed with the Mars exploration rovers. J. Geophys. Res. 113, E06S22 (2008).

    Article  Google Scholar 

  24. Ashley, J. CosmoELEMENTS: the study of exogenic rocks on Mars—an evolving subdiscipline in meteoritics. Elements 11, 10–11 (2015).

  25. Jensen, C. M. & Lee, D. W. Dry-ice bath based on ethylene glycol mixtures. J. Chem. Educ. 77, 629 (2000).

    Article  Google Scholar 

  26. Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).

    Article  ADS  Google Scholar 

  27. Mayo, S. L., Olafson, B. D. & Goddard, W. A. DREIDING: a generic force field for molecular simulations. J. Phys. Chem. 94, 8897–8909 (1990).

    Article  CAS  Google Scholar 

  28. Boyd, P. G., Moosavi, S. M., Witman, M. & Smit, B. Force-field prediction of materials properties in metal-organic frameworks. J. Phys. Chem. Lett. 8, 357–363 (2017).

    Article  CAS  PubMed  Google Scholar 

  29. Nosé, S. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81, 511–519 (1984).

    Article  ADS  Google Scholar 

  30. Hoover, W. G. Canonical dynamics: equilibrium phase-space distributions. Phys Rev A (Coll Park) 31, 1695–1697 (1985).

    Article  CAS  ADS  Google Scholar 

  31. Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117, 1–19 (1995).

    Article  CAS  ADS  Google Scholar 

  32. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett 77, 3865–3868 (1996).

    Article  CAS  PubMed  ADS  Google Scholar 

  33. Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953–17979 (1994).

    Article  ADS  Google Scholar 

  34. Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6, 15–50 (1996).

    Article  CAS  Google Scholar 

  35. Grimme, S., Ehrlich, S. & Goerigk, L. Effect of the damping function in dispersion corrected density functional theory. J Comput. Chem. 32, 1456–1465 (2011).

    Article  CAS  PubMed  Google Scholar 

  36. Maas, A. L., Hannun, A. Y. & Ng, A. Y. Rectifier nonlinearities improve neural network acoustic models. In Proc. 30th International Conference on Machine Learning (ICML) (eds Dasgupta, S. & McAllester, D.) 3 (ACM Press, 2013).

  37. Abadi, M. et al. TensorFlow: a system for large-scale machine learning. In Proc 12th USENIX Conference on Operating Systems Design and Implementation (eds Keeton, K. & Roscoe, T.) 265–283 (USENIX Association, 2016).

Download references


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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Source data

Source Data Fig. 2

Source data for Fig. 2.

Source Data Fig. 3

Source data for Fig. 3.

Source Data Fig. 4

Source data for Fig. 4.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing