比较古典和机器学习的力场,用于对直接空气捕获相关的金属有机框架的模拟变形
Logan M Brabson1, Andrew J Medford1, David S Sholl2
1School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
The journal of physical chemistry. C, Nanomaterials and interfaces
|September 24, 2025
概括
金属有机框架 (MOFs) 的变形影响气体吸附. 当前的计算模型难以准确地捕捉这种灵活性,特别是在直接捕获空气的应用中.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 纳米技术纳米技术
背景情况:
- 在金属有机框架 (MOF) 中,吸附剂诱导的变形会影响吸附性能,如容量和选择性.
- 大多数计算研究通过假设MOF刚性来简化计算,忽视框架灵活性.
- 现有的MOF的灵活力场 (FF) 往往是特定于材料的,对于模拟吸附剂诱导的变形缺乏一般适用性.
研究的目的:
- 确认吸附剂诱导的变形对CO2和H2O吸附能量的影响,在直接捕获空气 (DAC) 相关的MOF中.
- 用密度函数理论 (DFT) 对一般用途的经典和机器学习力场 (MLFFs) 的性能进行基准测试,以建模MOF变形.
- 评估MLFF在捕获DAC应用中吸附剂诱导的MOF变形方面的准确性.
主要方法:
- 密度函数理论 (DFT) 的计算被用来确定对MOF吸附能量的吸附剂诱导的变形效应.
- 几个通用经典FF和新兴MLFF (CHGNet,MACE-MP-0,Equiformer V2) 与DFT进行了对比.
- 通过将它们对MOF变形的预测与DFT结果进行比较,评估了FFs的准确性.
主要成果:
- DFT计算证实,吸附剂诱导的变形显著影响许多适合DAC的MOF中的CO2和H2O吸附能量.
- 目前的经典FF被发现不足以准确描述MOF变形,特别是在DAC相关的强烈吸附物-框架相互作用下.
- 新兴的MLFF,特别是CHGNet,MACE-MP-0和Equiformer V2,在模拟DFT描述的变形方面显示出比经典FF更有前途.
- 性能最好的MLFF,CHGNet,仍然表现出0.124 eV的平均绝对吸附能量误差,这表明有改进的余地.
结论:
- 吸附剂诱导的MOF变形对于直接捕获空气中的吸附精确预测至关重要,需要灵活的建模方法.
- 经典的FF不适合在DAC环境中可靠地建模MOF变形.
- MLFFs是准确模拟MOF灵活性的一个有希望的途径,尽管需要进一步开发才能实现实际应用的DFT级准确性.
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