Scholarlink

<p>The inherent tradeoff between gravimetric and volumetric hydrogen storage capacities in metal-organic frameworks (MOFs) limits their commercial viability. While benchmarked MOFs like MOF-5, IRMOF-20, and PCN-610 perform well at 77&#xa0;K, maintaining their performance at elevated temperatures (298&#xa0;K) remains challenging. To address this, a multi-objective particle swarm optimization framework was developed to identify promising MOFs for hydrogen storage. The optimization was guided by predictions from the bootstrapped-random forest trees. This optimization yielded 152 theoretical MOF feature combinations, which were matched with 733,792 existing structures. A nearest neighbor search identified 43 promising MOFs, with Zn-based MOF-2087 emerging as the global best, exhibiting consistent hydrogen storage performance across temperatures. Grand Canonical Monte Carlo simulations confirmed its high hydrogen uptake (5.3 wt% and 7.4 gH<sub>2</sub> L<sup>− 1</sup> at 298&#xa0;K). Molecular dynamics simulations further revealed C-clusters and metal sites as key adsorption centers, supporting the enhanced hydrogen storage behavior of MOF-2087. These findings highlight MOF-2087 as a computationally promising MOF for hydrogen storage up to 298&#xa0;K and demonstrate the effectiveness of the optimization-driven screening strategy.</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

A multi-objective optimization-driven screening approach for maximizing hydrogen storage capacities in MOFs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月2日

查看全文
  • P Anbumani,
  • Rohit Duvvuri,
  • Sudha Radhika,
  • Asif Abdul Azeez,
  • Ravindran Sujith

摘要

The inherent tradeoff between gravimetric and volumetric hydrogen storage capacities in metal-organic frameworks (MOFs) limits their commercial viability. While benchmarked MOFs like MOF-5, IRMOF-20, and PCN-610 perform well at 77 K, maintaining their performance at elevated temperatures (298 K) remains challenging. To address this, a multi-objective particle swarm optimization framework was developed to identify promising MOFs for hydrogen storage. The optimization was guided by predictions from the bootstrapped-random forest trees. This optimization yielded 152 theoretical MOF feature combinations, which were matched with 733,792 existing structures. A nearest neighbor search identified 43 promising MOFs, with Zn-based MOF-2087 emerging as the global best, exhibiting consistent hydrogen storage performance across temperatures. Grand Canonical Monte Carlo simulations confirmed its high hydrogen uptake (5.3 wt% and 7.4 gH2 L− 1 at 298 K). Molecular dynamics simulations further revealed C-clusters and metal sites as key adsorption centers, supporting the enhanced hydrogen storage behavior of MOF-2087. These findings highlight MOF-2087 as a computationally promising MOF for hydrogen storage up to 298 K and demonstrate the effectiveness of the optimization-driven screening strategy.