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<p>Methane hydrates require extreme conditions, and promoter discovery remains largely empirical. We develop a multimodal deep-learning framework that predicts methane-hydrate equilibrium pressures from molecular structure. Trained on over eighty promoters, the model extrapolates beyond its domain and prospectively identifies ethylene sulfite as a new thermodynamic promoter, experimentally validated within 1 MPa accuracy while forming structure II hydrates.</p>

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Data-driven discovery of methane hydrate promoters

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月29日

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  • Yusung Ok,
  • Youngjune Park

摘要

Methane hydrates require extreme conditions, and promoter discovery remains largely empirical. We develop a multimodal deep-learning framework that predicts methane-hydrate equilibrium pressures from molecular structure. Trained on over eighty promoters, the model extrapolates beyond its domain and prospectively identifies ethylene sulfite as a new thermodynamic promoter, experimentally validated within 1 MPa accuracy while forming structure II hydrates.