Mg-MOF-74 中 CO2 化学吸附和扩散的分子动力学模拟的量子信息机器学习潜力,ACS Nano

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Mg-MOF-74 中 CO2 化学吸附和扩散的分子动力学模拟的量子信息机器学习潜力
ACS Nano ( IF 15.8 ) Pub Date : 2023-03-08 , DOI: 10.1021/acsnano.2c11102
Bowen Zheng _{1,

2} , Felipe Lopes Oliveira _{3,

4} , Rodrigo Neumann Barros Ferreira ₃ , Mathias Steiner ₃ , Hendrik Hamann ₁ , Grace X Gu ₂ , Binquan Luan ₁

Affiliation

在用于气体分离和纯化的各种多孔固体中，金属有机骨架 (MOF) 是很有前途的材料，可能兼具高 CO ₂吸收性和 CO ₂ /N ₂选择性。到目前为止，在当今已知的数十万个 MOF 结构中，通过计算确定最适合的物种仍然是一个挑战。基于第一原理的 CO _2模拟MOF 中的吸附将提供必要的准确性；然而，由于高计算成本，它们是不切实际的。经典的基于力场的模拟在计算上是可行的；但是，它们没有提供足够的准确性。因此，在模拟中很难获得既需要准确的力场又需要足够长的采样计算时间的熵贡献。在这里，我们报告了用于MOF 中CO ₂原子模拟的量子信息机器学习力场 (QMLFF) 。我们证明该方法在保持量子级精度的同时，比第一原理方法具有更高的计算效率（∼1000×）。作为概念证明，我们展示了基于 QMLFF 的 CO _{2分子动力学模拟}在 Mg-MOF-74 中可以预测结合自由能景观和接近实验值的扩散系数。机器学习和原子模拟的结合有助于更准确、更有效地对MOF 中气体分子的化学吸附和扩散进行计算机评估。

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Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2’s Chemisorption and Diffusion in Mg-MOF-74

Among various porous solids for gas separation and purification, metal-organic frameworks (MOFs) are promising materials that potentially combine high CO₂ uptake and CO₂/N₂ selectivity. So far, within the hundreds of thousands of MOF structures known today, it remains a challenge to computationally identify the best suited species. First principle-based simulations of CO₂ adsorption in MOFs would provide the necessary accuracy; however, they are impractical due to the high computational cost. Classical force field-based simulations would be computationally feasible; however, they do not provide sufficient accuracy. Thus, the entropy contribution that requires both accurate force fields and sufficiently long computing time for sampling is difficult to obtain in simulations. Here, we report quantum-informed machine-learning force fields (QMLFFs) for atomistic simulations of CO₂ in MOFs. We demonstrate that the method has a much higher computational efficiency (∼1000×) than the first-principle one while maintaining the quantum-level accuracy. As a proof of concept, we show that the QMLFF-based molecular dynamics simulations of CO₂ in Mg-MOF-74 can predict the binding free energy landscape and the diffusion coefficient close to experimental values. The combination of machine learning and atomistic simulation helps achieve more accurate and efficient in silico evaluations of the chemisorption and diffusion of gas molecules in MOFs.

更新日期：2023-03-08

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