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One Step Closer to Living on Mars: AI Unlocks Secrets of Oxygen Production on the Red Planet

Recent breakthroughs in synthesizing oxygen on Mars using a robotic AI-chemist to create OER catalysts from Martian meteorites mark a significant step towards realizing the dream of Mars colonization. This technology promises to establish an oxygen factory on Mars, bringing human habitation on the planet closer to reality. Credit: AI-Chemist Group at the University of Science and Technology of China

An AI chemist has successfully created a catalyst for producing oxygen from Martian meteorites.

Immigration to and living on MarsMars is the second smallest planet in our solar system and the fourth planet from the sun. It is a dusty, cold, desert world with a very thin atmosphere. Iron oxide is prevalent in Mars' surface resulting in its reddish color and its nickname "The Red Planet." Mars' name comes from the Roman god of war.” data-gt-translate-attributes=”[{“attribute”:”data-cmtooltip”, “format”:”html”}]” tabindex=”0″ role=”link”>Mars have often been themes in science fiction. Before these dreams can become reality, humanity faces significant challenges, such as the scarcity of vital resources like oxygen needed for long-term survival on the Red Planet. Yet, recent discoveries of water activity on Mars have sparked new hope for overcoming these obstacles.

Scientists are now exploring the possibility of decomposing water to produce oxygen through electrochemical water oxidation driven by solar power with the help of oxygen evolution reaction (OER) catalysts. The challenge is to find a way to synthesize these catalysts in situ using materials on Mars, instead of transporting them from the Earth, which is of high cost.

Advancements in AI and Martian Chemistry

To tackle this problem, a team led by Prof. Luo Yi, Prof. Jiang Jun, and Prof. Shang Weiwei from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences (CAS), recently made it possible to synthesize and optimize OER catalysts automatically from Martian meteorites with their robotic artificial intelligence (AI)-chemist.

Their research, in collaboration with Deep Space Exploration Laboratory, was recently published in the journal Nature Synthesis.

“The AI chemist innovatively synthesizes OER catalyst using Martian material based on interdisciplinary cooperation,” said Prof. Luo Yi, leading scientist of the team.

In each experimental cycle, the AI chemist first analyzes the elemental composition of the Martian ores using the laser-induced breakdown spectroscopy (LIBS) as its eyes. Then, it carries out a series of pretreatments on the ores, including weighing in the solid-dispensing workstation, preparing feedstock solutions in the liquid-dispensing workstation, separation from liquid in the centrifugation workstation, and solidification in the dryer workstation.

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A robotic AI-Chemist makes useful Oxygen generation catalysts with Martian meteorites. Credit: AI-Chemist Group at University of Science and Technology of China

The resulting metal hydroxides are treated with Nafion adhesive to prepare the working electrode for OER testing at the electrochemical workstation. The testing data are sent to the computational ‘brain’ of the AI chemist in real-time for machine learningMachine learning is a subset of artificial intelligence (AI) that deals with the development of algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Machine learning is used to identify patterns in data, classify data into different categories, or make predictions about future events. It can be categorized into three main types of learning: supervised, unsupervised and reinforcement learning.” data-gt-translate-attributes=”[{“attribute”:”data-cmtooltip”, “format”:”html”}]” tabindex=”0″ role=”link”>machine learning (ML) processing.

The AI chemist’s ‘brain’ employs quantum chemistry and molecular dynamics simulations for 30,000 of high-entropy hydroxides with different elemental ratios and calculates their OER catalytic activities via density functional theory. The simulation data are used to train a neural network model for rapidly predicting the catalysts’ activities with different elemental compositions.

Finally, through Bayesian optimization, the ‘brain’ predicts the combination of available Martian ores needed for synthesizing the optimal OER catalyst.

Achieving a Breakthrough in Oxygen Production

So far, the AI chemist has created an excellent catalyst using five types of Martian meteorites under unmanned conditions. This catalyst can operate steadily for over 550,000 seconds at a current density of 10 mA cm-2 and an overpotential of 445.1 mV. A further test at -37 °C, the temperature on Mars, confirmed that the catalyst can steadily produce oxygen without any apparent degradation.

Within two months, the AI chemist has completed the complex optimization of catalysts that would take 2000 years for a human chemist.

The team is working to turn the AI chemist into a general experiment platform for various chemical syntheses without human intervention. The reviewer of the paper highly remarked: “This type of research is of wide interest and is under rapid development in organic/inorganic material synthesis and discovery.”

“In the future, humans can establish an oxygen factory on Mars with the assistance of an AI chemist,” said Jiang. Only 15 hours of solar irradiation is needed to produce sufficient oxygen concentration required for human survival. “This breakthrough technology brings us one step closer to achieving our dream of living on Mars,” he said.

Reference: “Automated synthesis of oxygen-producing catalysts from Martian meteorites by a robotic AI chemist” by Qing Zhu, Yan Huang, Donglai Zhou, Luyuan Zhao, Lulu Guo, Ruyu Yang, Zixu Sun, Man Luo, Fei Zhang, Hengyu Xiao, Xinsheng Tang, Xuchun Zhang, Tao Song, Xiang Li, Baochen Chong, Junyi Zhou, Yihan Zhang, Baicheng Zhang, Jiaqi Cao, Guozhen Zhang, Song Wang, Guilin Ye, Wanjun Zhang, Haitao Zhao, Shuang Cong, Huirong Li, Li-Li Ling, Zhe Zhang, Weiwei Shang, Jun Jiang and Yi Luo, 13 November 2023, Nature Synthesis.
DOI: 10.1038/s44160-023-00424-1

Source: SciTechDaily