Zhang Mingzheng, Xie Qiming, Wang Zhuozheng, Zhang Wentao, Bo Yawen, Zhang Zhiying, Li Hao, Luo Yi, Gong Qihan, Li Shunning, Pan Feng
School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, China.
Fundamental Science & Advanced Technology Lab, PetroChina Petrochemical Research Institute, China National Petroleum Corporation, Beijing 102200, China.
J Phys Chem Lett. 2024 May 9;15(18):4815-4822. doi: 10.1021/acs.jpclett.4c00860. Epub 2024 Apr 26.
Metal-organic frameworks (MOFs) are potential candidates for gas-selective adsorbents for the separation of an ethylene/ethane mixture. To accelerate material discovery, high-throughput computational screening is a viable solution. However, classical force fields, which were widely employed in recent studies of MOF adsorbents, have been criticized for their failure to cover complicated interactions such as those involving π electrons. Herein, we demonstrate that machine learning force fields (MLFFs) trained on quantum-chemical reference data can overcome this difficulty. We have constructed a MLFF to accurately predict the adsorption energies of ethylene and ethane on the organic linkers of MOFs and discovered that the π electrons from both the ethylene molecule and the aromatic rings in the linkers could substantially influence the selectivity for gas adsorption. Four kinds of MOF linkers are identified as having promise for the separation of ethylene and ethane, and our results could also offer a new perspective on the design of MOF building blocks for diverse applications.
金属有机框架（MOF）是用于分离乙烯/乙烷混合物的气体选择性吸附剂的潜在候选材料。为了加速材料发现，高通量计算筛选是一种可行的解决方案。然而，经典力场在最近的MOF吸附剂研究中被广泛应用，但因其未能涵盖诸如涉及π电子的复杂相互作用而受到批评。在此，我们证明在量子化学参考数据上训练的机器学习力场（MLFF）可以克服这一困难。我们构建了一个MLFF来准确预测乙烯和乙烷在MOF有机连接体上的吸附能，并发现乙烯分子和连接体中芳香环的π电子都可以显著影响气体吸附的选择性。四种MOF连接体被确定为有望用于乙烯和乙烷的分离，我们的结果也可以为设计用于各种应用的MOF构建块提供新的视角。
