通过机器学习和分子模拟的协同作用,加速对III类多孔液体的金属有机框架选择
Lisha Sheng1,2,3, Yi Wang1,2, Xinzhu Mou4
1School of Energy and Environment, Southeast University, Nanjing 210000, P. R. China.
ACS applied materials & interfaces
|November 21, 2023
概括
这项研究引入了一种计算方法,以发现新的多孔液体,以有效捕获碳. 基于RICBEM的多孔液体证明了创纪录的二氧化碳捕获性能,推动了材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算化学的计算化学
背景情况:
- 在溶剂中溶解的金属有机框架 (MOFs),称为多孔液体,对碳捕获有希望.
- 实验发现适合多孔液体的MOF是耗时且资源密集的.
研究的目的:
- 开发一种高效的计算选策略,用于识别III型多孔液体的MOF候选物.
- 预测和优化二氧化碳吸收和碳捕获应用的选择性.
主要方法:
- 利用密度函数理论和分子动力学模拟,建立了4530个MOF候选人的数据库.
- 采用高通量分子模拟和随机森林机器学习模型来预测MOF性能.
- 应用沙普利添加剂解释 (SHAP) 用于特征重要性分析,以确定二氧化碳吸附和选择性的关键描述符.
主要成果:
- 确定了重力测量表面积,多孔度,密度,金属分数和孔径分布作为二氧化碳吸附和选择性的关键因素.
- 预测RICBEM作为顶级候选者,模拟CO2吸附率为20.87mmol/g,CO2/N2选择性为16.75.
- 在实验中合成了基于RICBEM的多孔液体,达到2.21 mmol/g的CO2吸附能力和63.2.2的CO2/N2选择性.
结论:
- 开发的计算选方法显著加速了对多孔液体应用的MOF的发现.
- 基于RICBEM的多孔液体表现出前所未有的碳捕获性能,创造了一个新的基准.
- 这种方法可以通过考虑相关因素来适应用于各种应用的MOF和溶剂的选.
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