Imagine astronauts living on Mars and producing some of the materials they need directly from Martian soil and rocks. Instead of carrying every essential chemical from Earth, future missions could use local resources to manufacture oxygen and other important materials. A new study by Zhu and his team brings this idea closer to reality with an innovative robotic artificial-intelligence (AI) chemist.
The researchers have developed a fully automated system that can study Martian materials, create chemical catalysts and identify the best catalyst for producing oxygen. Remarkably, the entire process can be completed without direct human intervention.
Why Making Oxygen on Mars Matters
Oxygen is one of the most important resources for human exploration beyond Earth. Astronauts need it to breathe, but oxygen can also be used in other technologies, including systems designed to produce rocket propellant.
Transporting large quantities of oxygen from Earth to Mars would be extremely difficult and expensive. This is why scientists are investigating ways to produce essential resources using materials already available on Mars.
Martian rocks and soil contain various minerals and chemical elements that could potentially serve as raw materials. However, turning these resources into useful chemicals requires complex chemical processes. Scientists must identify suitable materials, develop catalysts and test them under appropriate conditions.
Doing this manually on Mars could be slow and difficult, particularly when communication between Earth and Mars can take significant time. An autonomous laboratory could therefore provide a major advantage.
A Robotic Chemist for the Red Planet
Zhu and his team have demonstrated a robotic AI chemist designed to address this challenge.
The system can perform several stages of the scientific process automatically. It begins by processing Martian ore, preparing it for further chemical reactions. It then uses the processed material to synthesize catalysts.
After producing the catalysts, the robotic system characterizes and tests them to determine how effectively they can support the oxygen evolution reaction, commonly known as OER.
Most importantly, the system can learn from the results and use that information to decide what catalyst composition it should investigate next.
This creates an automated cycle of synthesis, testing, learning and optimization.
Rather than requiring researchers to manually choose every new experiment, the AI system can make increasingly informed decisions based on the data it collects.
The Challenge of Finding the Best Catalyst
Finding an effective catalyst is not simple. A catalyst can contain different elements in different proportions, creating a huge number of possible chemical compositions.
According to the researchers, their system considered more than three million possible catalyst compositions. Testing every possibility individually would be impractical.
This is where artificial intelligence becomes particularly useful.
The researchers developed a machine-learning model using information from both first-principles calculations and real experimental measurements. First-principles data can provide predictions about how materials may behave based on fundamental physical laws, while experimental data shows how those materials actually perform in the laboratory.
By combining these two sources of information, the AI can make better predictions about which compositions are worth testing.
Instead of searching randomly, the robotic chemist can focus its experiments on promising candidates.
How the AI Search Works
The process can be compared to searching for the best route through a huge maze.
If scientists tried every possible path, they could spend an enormous amount of time reaching the destination. An intelligent system, however, can learn from the paths it has already explored and use that knowledge to choose more promising routes.
The AI chemist follows a similar approach.
It examines the available data, predicts which catalyst compositions could perform well and directs the robotic laboratory to synthesize and test selected candidates. The results of those experiments are then fed back into the machine-learning model.
With every new experiment, the system gains additional information.
This allows the AI to continuously improve its search for an effective catalyst without requiring researchers to manually design every experiment.
A Catalyst Made From Martian Resources
The system ultimately identified an effective catalyst composition from the enormous range of possibilities.
The synthesized catalyst demonstrated an overpotential of 445.1 millivolts while operating at a current density of 10 milliamperes per square centimetre.
Even more significantly, the catalyst continued operating for more than 550,000 seconds under these conditions. That is equivalent to more than six days of continuous operation.
These results demonstrate that materials derived from Martian resources can potentially be transformed into useful catalysts for chemical production.
The experiment therefore represents more than simply the discovery of a new catalyst. It demonstrates a possible approach for performing sophisticated materials research with a high level of automation.
Why Automation Could Be Critical on Mars
A robotic laboratory could be especially valuable on Mars because astronauts will have limited time, energy and resources.
Human crews would need to concentrate on essential activities such as exploration, maintenance, scientific research and survival. Having robots perform repetitive laboratory work could reduce the workload on astronauts.
Autonomous systems could also continue experiments while astronauts are busy or even when direct communication with Earth is unavailable.
This is important because Mars is extremely far from Earth. Commands and responses cannot be exchanged instantly. A laboratory that depends on constant instructions from scientists on Earth would therefore face significant limitations.
An AI-controlled laboratory could instead make decisions locally, allowing experiments to continue with much less dependence on real-time communication.
Beyond Oxygen Production
Although the study focuses on catalysts for the oxygen evolution reaction, the underlying concept could have much broader applications.
Future autonomous laboratories might potentially produce or refine a variety of chemicals and materials needed for long-duration missions.
The larger goal is to move toward in-situ resource utilization, or ISRU. This means using resources found at a destination rather than transporting everything from Earth.
Such technology could help reduce the amount of equipment, chemicals and consumables that spacecraft would need to carry.
The AI chemist could become one part of a larger ecosystem in which robots collect local materials, process them and transform them into useful resources.
A Step Toward Self-Sufficient Mars Exploration
The work by Zhu and his team highlights an important idea for future space exploration: astronauts may not need to rely entirely on supplies brought from Earth.
By combining robotics, artificial intelligence, machine learning and chemistry, scientists are developing systems capable of making complex scientific decisions autonomously.
The ability to identify useful materials from millions of possible combinations could dramatically speed up the search for technologies suitable for extraterrestrial environments.
The demonstrated catalyst also shows that Martian materials can potentially serve as starting points for producing valuable chemical products.
The Future of Autonomous Chemistry in Space
Human missions to Mars are likely to require technologies that can operate far from Earth and with limited support. Autonomous AI chemists could play an important role in that future.
The research demonstrates how a robotic system can take Martian ore through multiple stages—from pretreatment and catalyst synthesis to characterization, testing and optimization—while minimizing the need for human intervention.
Most importantly, the system does not simply perform experiments according to a fixed list of instructions. It uses experimental results to guide its search for better materials.
That combination of robotics, artificial intelligence and chemistry could become a powerful tool for future exploration.
Producing oxygen from local resources remains a major technological challenge, but this research offers an encouraging demonstration of how autonomous science could help overcome it. One day, laboratories operating on Mars may not just analyze the Red Planet—they could actively turn its rocks and minerals into the resources astronauts need to live and work there.
Reference: 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). https://doi.org/10.1038/s44160-023-00424-1
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