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<p>As trichloromethane (TCM) is classified as a Group 2B carcinogen by the World Health Organization (WHO), adsorption using high-performance metal-organic frameworks (MOFs) has become a commonly employed control technique for TCM mitigation. To efficiently select suitable MOFs for TCM adsorption from a vast array of available materials, this study first applied machine learning to establish a dataset, identify key descriptors, and develop a MOF screening model using a decision tree (DT) algorithm guided by these descriptors. The screening model achieved an accuracy of 89.41%, ultimately identifying MOF-5 as suitable candidates for TCM adsorption from over 14,000 MOFs. Further, molecular simulation techniques were used to construct molecular models of five MOF-5 materials: MOF-5, MOF-5-NH<sub>2</sub>, MOF-5-Cl, MOF-5-F, and MOF-5-OH. The adsorption isotherms, interaction energies, adsorption density distribution, energy density distribution, electrostatic potential, radial distribution function (RDF), and diffusion coefficients were thoroughly examined to elucidate the microscopic adsorption mechanisms of MOF-5 based materials for TCM. This work provides valuable insights for the efficient, targeted optimization and selection of MOFs in adsorption applications, particularly for the adsorption and control of TCM.</p>

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Study on MOFs selection for TCM adsorption and adsorptive mechanisms via descriptor guidance and molecular simulation

  • 期刊论文

  • 发表日期：

    2026年5月27日

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  • Letian Yang,
  • Haokun Ti,
  • Weihao Yan,
  • Qingzhen Xu,
  • Danyang Wang,
  • Xuegu Zhang,
  • Zidie Zhang,
  • Shi Li

摘要

As trichloromethane (TCM) is classified as a Group 2B carcinogen by the World Health Organization (WHO), adsorption using high-performance metal-organic frameworks (MOFs) has become a commonly employed control technique for TCM mitigation. To efficiently select suitable MOFs for TCM adsorption from a vast array of available materials, this study first applied machine learning to establish a dataset, identify key descriptors, and develop a MOF screening model using a decision tree (DT) algorithm guided by these descriptors. The screening model achieved an accuracy of 89.41%, ultimately identifying MOF-5 as suitable candidates for TCM adsorption from over 14,000 MOFs. Further, molecular simulation techniques were used to construct molecular models of five MOF-5 materials: MOF-5, MOF-5-NH2, MOF-5-Cl, MOF-5-F, and MOF-5-OH. The adsorption isotherms, interaction energies, adsorption density distribution, energy density distribution, electrostatic potential, radial distribution function (RDF), and diffusion coefficients were thoroughly examined to elucidate the microscopic adsorption mechanisms of MOF-5 based materials for TCM. This work provides valuable insights for the efficient, targeted optimization and selection of MOFs in adsorption applications, particularly for the adsorption and control of TCM.