在中加速发现以乙烯/乙烯和/分离的金属有机框架:一种集成分子模拟,机器学习和主动学习的协同方法
Varad Daoo1, Jayant K Singh1,2
1Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India.
ACS applied materials & interfaces
|January 30, 2024
概括
本研究使用机器学习和高通量选来发现新的金属有机框架 (MOFs),以节能地分离碳化合物混合物. 积极学习显著加快了对表现最好的MOFs的识别,为冷蒸提供了更有效的替代方案.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 用于分离C2H4/C2H6和C3H6/C3H8混合物的冷蒸是耗能密集的.
- 基于吸附剂的分离是一种有前途的替代技术.
- 金属有机框架 (MOFs) 正在作为选择性吸附剂进行探索.
研究的目的:
- 选大量的假设MOF数据库进行选择性抛分离.
- 开发和应用机器学习 (ML) 和主动学习 (AL) 以实现高效的MOF发现.
- 为了确定C2和C3碳化合物混合物分离的高性能MOF.
主要方法:
- 大约23000个假设的MOF的高通量选.
- 基于结构的预选以过具有不合适的几何性质的MOF.
- 在多元组件大法典蒙特卡洛 (m-GCMC) 模拟数据上训练的随机森林ML模型的开发.
- 积极学习 (AL) 的应用,以提高预测效率和降低计算成本.
主要成果:
- 一个随机森林模型成功预测了olefin/paraffin分离的MOF性能.
- 与监督的ML相比,主动学习的效率明显更高 (约为29倍).
- 框架化学,特别是具有特定拓安排 (tfzd,hms) 的和铜金属节点,成为高选择性的关键.
- 已识别的MOF对C2H4/C2H6和C3H6/C3H8混合物分离具有记录选择性.
结论:
- 开发的层次战略有效地利用分子模拟和AL进行MOF快速查.
- 这种方法加速了用于工业相关的分离工艺的材料的发现.
- 已识别的MOF为当前分离技术提供了更节能的替代方案.
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