Scholarlink

<p>Oxidative coupling of methane (OCM) reaction is crucial for converting natural gas into value-added chemicals like ethylene. Despite the development of over 2000 catalysts, achieving a C<sub>2</sub> (C<sub>2</sub>H<sub>4</sub> + C<sub>2</sub>H<sub>6</sub>) yield of 30% necessary for industrial viability remains a great challenge. In recent years, the rapid advancement of artificial intelligence (AI) has brought new opportunities to address this issue: the integration of machine learning (M-L) facilitates the identification and optimization of catalyst compositions, while machine synthesis (M-S) enhances the efficiency of catalyst synthesis. This perspective discusses the recent advancements in OCM using M-L and M-S techniques, aiming to accelerate the discovery and identification the most efficient catalyst in a benchmarking study, and promote the industrial application of OCM reaction.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Accelerate the discovery of OCM catalysts with machine-learning and machine-synthesis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月8日

查看全文
  • Jifeng Ouyang,
  • Yutao Ren,
  • Shihui Zou,
  • Xutao Chen,
  • Jie Fan

摘要

Oxidative coupling of methane (OCM) reaction is crucial for converting natural gas into value-added chemicals like ethylene. Despite the development of over 2000 catalysts, achieving a C2 (C2H4 + C2H6) yield of 30% necessary for industrial viability remains a great challenge. In recent years, the rapid advancement of artificial intelligence (AI) has brought new opportunities to address this issue: the integration of machine learning (M-L) facilitates the identification and optimization of catalyst compositions, while machine synthesis (M-S) enhances the efficiency of catalyst synthesis. This perspective discusses the recent advancements in OCM using M-L and M-S techniques, aiming to accelerate the discovery and identification the most efficient catalyst in a benchmarking study, and promote the industrial application of OCM reaction.