Accelerating In Silico Discovery of Metal–Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning,ACS Applied Materials & Interfaces

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Accelerating In Silico Discovery of Metal–Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2024-01-30 , DOI: 10.1021/acsami.3c14505
Varad Daoo ₁ , Jayant K Singh _{1,

2}

Affiliation

Cryogenic distillation, a currently employed method for C₂H₄/C₂H₆ and C₃H₆/C₃H₈ mixture separation, is energy-intensive, prompting the research toward alternative technologies, including adsorbent-based separation. In this work, we combine machine learning (ML) technique with high-throughput screening to screen ∼23,000 hypothetical metal–organic frameworks (MOFs) for paraffin (C₂H₆ and C₃H₈) selective adsorbent separation. First, structure-based prescreening was employed to remove MOFs with undesired geometric properties. Further, a random forest model built upon the multicomponent grand canonical Monte Carlo (m-GCMC) simulation data of training set MOFs was found to be the most successful in learning the relationship between MOF features and olefin/paraffin mixture separation. Using this technique, the separation performance of the remaining (test set) MOFs was predicted, and the top-performing MOFs were identified. We also employed active learning (AL) to evaluate its effectiveness in improving the prediction of olefin/paraffin selectivity. AL was discovered to be ∼29 times more efficient than the best-supervised ML model, as it was able to identify the top materials in limited training data and at a fraction of computational cost and time as compared to ML techniques. Among the top selected materials, framework chemistry was found to be the most important parameter. Nickel and copper (as a metal node) in a tfzd and hms topological arrangement respectively, were discovered to be a prevalent attribute in high-performing MOFs, further demonstrating the prominent significance of framework chemistry. Additionally, the top MOFs discovered were studied in detail and further compared to the previously reported MOFs. These MOFs show the highest selectivity for C₂H₄/C₂H₆ and C₃H₆/C₃H₈ mixture separation, as reported until date. The hierarchical strategy devised in this study will facilitate the quick screening of MOFs across multiple databases toward industrially significant separation processes by leveraging molecular simulations and AL.

中文翻译：

加速乙烷/乙烯和丙烷/丙烯分离金属有机框架的计算机发现：集成分子模拟、机器学习和主动学习的协同方法

低温蒸馏是目前用于C ₂ H ₄ /C ₂ H ₆和C ₃ H ₆ /C ₃ H ₈混合物分离的方法，该方法是能源密集型的，促进了对替代技术的研究，包括基于吸附剂的分离。在这项工作中，我们将机器学习（ML）技术与高通量筛选相结合，筛选约 23,000 种假设的金属有机框架（MOF），用于石蜡（C ₂ H ₆和 C ₃ H ₈ ）选择性吸附剂分离。首先，采用基于结构的预筛选来去除具有不需要的几何特性的 MOF。此外，基于训练集 MOF 的多组分正则蒙特卡罗 (m-GCMC) 模拟数据构建的随机森林模型被发现在学习 MOF 特征与烯烃/石蜡混合物分离之间的关系方面最成功。使用该技术，可以预测剩余（测试集）MOF 的分离性能，并确定性能最佳的 MOF。我们还采用主动学习（AL）来评估其在改进烯烃/石蜡选择性预测方面的有效性。人们发现 AL 的效率比最佳监督的 ML 模型高约 29 倍，因为与 ML 技术相比，它能够在有限的训练数据中识别出最重要的材料，并且计算成本和时间只占一小部分。在首选材料中，骨架化学被认为是最重要的参数。镍和铜（作为金属节点）分别以 tfzd 和 hms 拓扑排列被发现是高性能 MOF 中的普遍属性，进一步证明了框架化学的突出意义。此外，还对发现的顶级 MOF 进行了详细研究，并与之前报道的 MOF 进行了进一步比较。据迄今为止报道，这些 MOF 对 C ₂ H ₄ /C ₂ H ₆和 C ₃ H ₆ /C ₃ H ₈混合物分离显示出最高的选择性。本研究中设计的分层策略将有助于利用分子模拟和 AL 跨多个数据库快速筛选 MOF，以实现具有工业意义的分离过程。

更新日期：2024-01-30

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