Sadah Mohammed , Fadwa Eljack , Monzure-Khoda Kazi , Mert Atilhan
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引用次数: 0
摘要
在本文中,我们提出了一种基于深度学习的新型群贡献框架,用于离子液体(ILs)的定向设计。该计算框架能以数据驱动的方式,通过准确的性质预测,加快并改善寻找理想离子液体分子结构的过程。我们提出的框架包括两个基本步骤:通过合并两个深度学习模型(DNN-GC 和 ANN-GC)建立离子液体粘度和二氧化碳溶解度之间的相关性,并利用这种相关性确定具有最大二氧化碳吸收能力的最佳离子液体结构。我们的模型具有很高的准确性,DNN-GC、ANN-GC 和 DNN-ANN-GC 的 R2 值分别为 95%、94.2% 和 96.4%。相关结果与实验数据一致,证明了我们框架的适用性。最后,在二氧化碳捕获案例研究中使用了该算法,以生成和选择性能最佳的新型 IL,这些 IL 与文献中已有的 IL 表现出一致的行为。
Development of a deep learning-based group contribution framework for targeted design of ionic liquids
In this article, we present a novel deep learning-based group contribution framework for the targeted design of ionic liquids (ILs). This computational framework can expedite and improve the process of finding desirable molecular structures of IL via accurate property predictions in a data-driven manner. Our proposed framework consists of two essential steps: establishing a correlation between IL viscosity and CO2 solubility by merging two deep learning models (DNN-GC and ANN-GC) and utilizing this correlation to identify the optimal IL structure with maximal CO2 absorption capacity. Our model achieves high accuracy with R2 values of 95%, 94.2%, and 96.4% for DNN-GC, ANN-GC, and DNN-ANN-GC, respectively. Correlation results align with the experimental data, affirming the applicability of our framework. Finally, the algorithm is employed in a CO2 capture case study to generate and select the best-performing novel ILs, which exhibit behavior consistent with established ILs in the literature.
期刊介绍:
Computers & Chemical Engineering is primarily a journal of record for new developments in the application of computing and systems technology to chemical engineering problems.