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<p>Oxygen-bearing organic molecules, including aldehydes, alcohols, and peroxides, serve as key precursors to complex organics in extraterrestrial environments. Their non-equilibrium formation mechanisms critically constrain the molecular complexity available for prebiotic chemistry. Here we introduce a methodology combining astrochemical simulation experiments with machine learning techniques, to unravel the composition and formation mechanisms of complex organics synthesized in methane (CH<sub>4</sub>) – molecular oxygen (O<sub>2</sub>) ices exposed to proxies of galactic cosmic rays. Exploiting synchrotron vacuum ultraviolet photoionization mass spectrometry (SVUV-PI-ReToF-MS), we identified a rich inventory of oxygen-bearing organic molecules in the temperature-programmed desorption (TPD) phase through photoionization efficiency (PIE) curve fitting and isotopic labeling experiments. Non-equilibrium formation mechanisms were discovered by employing neural network potentials in combination with advanced reaction path search algorithms. This integration of machine learning with astrochemical experiments is anticipated to revolutionize our capabilities in predicting the molecular inventory in dust grain ice mantles, thus advancing our fundamental knowledge of the molecular evolution in the new astrochemical ice age.</p>

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Experimental astrochemistry and machine learning unravel the formation of oxygen-bearing organic molecules in extraterrestrial ices

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月14日

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  • Xilin Bai,
  • Sixuan Mi,
  • Qi’ang Gong,
  • Jinghui Lu,
  • Jiuzhong Yang,
  • Yang Pan,
  • Xiaohu Li,
  • Zhenrong Sun,
  • Tong Zhu,
  • Ralf I. Kaiser,
  • Tao Yang

摘要

Oxygen-bearing organic molecules, including aldehydes, alcohols, and peroxides, serve as key precursors to complex organics in extraterrestrial environments. Their non-equilibrium formation mechanisms critically constrain the molecular complexity available for prebiotic chemistry. Here we introduce a methodology combining astrochemical simulation experiments with machine learning techniques, to unravel the composition and formation mechanisms of complex organics synthesized in methane (CH4) – molecular oxygen (O2) ices exposed to proxies of galactic cosmic rays. Exploiting synchrotron vacuum ultraviolet photoionization mass spectrometry (SVUV-PI-ReToF-MS), we identified a rich inventory of oxygen-bearing organic molecules in the temperature-programmed desorption (TPD) phase through photoionization efficiency (PIE) curve fitting and isotopic labeling experiments. Non-equilibrium formation mechanisms were discovered by employing neural network potentials in combination with advanced reaction path search algorithms. This integration of machine learning with astrochemical experiments is anticipated to revolutionize our capabilities in predicting the molecular inventory in dust grain ice mantles, thus advancing our fundamental knowledge of the molecular evolution in the new astrochemical ice age.