Ahmed Alauddin, Nath Karabi, Matzger Adam J, Siegel Donald J
Mechanical Engineering Department, University of Michigan, Ann Arbor, Michigan 48109, United States.
Department of Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States.
ACS Appl Mater Interfaces. 2024 Oct 2. doi: 10.1021/acsami.4c10611.
A machine learning (ML) model is developed for predicting useable methane (CH) capacities in metal-organic frameworks (MOFs). The model applies to a wide variety of MOFs, including those with and without open metal sites, and predicts capacities for multiple pressure swing conditions. Despite its wider applicability, the model requires only 5 measurable structural features as input, yet achieves accuracies that surpass less-general models. Application of the model to a database of more than a million hypothetical MOFs identified several hundred whose capacities surpass that of the benchmark MOF, UMCM-152. Guided by the computational predictions, one of the promising candidates, UMCM-153, was synthesized and demonstrated to achieve superior volumetric capacity for CH. Feature importance analyses reveal that pore volume and gravimetric surface area are the most important features for predicting CH capacity in MOFs. Finally, a reverse ML model is demonstrated. This model predicts the set of elementary MOF structural properties needed to achieve a desired CH capacity for a prescribed operating condition.
开发了一种机器学习（ML）模型，用于预测金属有机框架（MOF）中可用甲烷（CH）的容量。该模型适用于多种MOF，包括有和没有开放金属位点的MOF，并预测多种变压条件下的容量。尽管其适用性更广，但该模型仅需要5个可测量的结构特征作为输入，却能实现超越通用性较差模型的准确率。将该模型应用于一个包含超过一百万个假设MOF的数据库，识别出了数百个容量超过基准MOF（UMCM-152）的MOF。在计算预测的指导下，合成了一种有前景的候选物UMCM-153，并证明其对CH具有卓越的体积容量。特征重要性分析表明，孔体积和重量比表面积是预测MOF中CH容量的最重要特征。最后，展示了一种反向ML模型。该模型预测了在规定操作条件下实现所需CH容量所需的基本MOF结构特性集。
