An XGBoost Algorithm Based on Molecular Structure and Molecular Specificity Parameters for Predicting Gas Adsorption,Langmuir

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An XGBoost Algorithm Based on Molecular Structure and Molecular Specificity Parameters for Predicting Gas Adsorption
Langmuir ( IF 3.7 ) Pub Date : 2023-05-02 , DOI: 10.1021/acs.langmuir.3c00255
Lujun Li _{1,

2,

3,

4} , Yiming Zhao _{2,

4} , Haibin Yu _{2,

3,

4} , Zhuo Wang _{2,

4} , Yongjia Zhao _{2,

4} , Mingqi Jiang _{2,

4,

5}

Affiliation

In this paper, an improved Extreme Gradient Boosting (XGBoost) algorithm based on the Graph Isomorphic Network (GIN) for predicting the adsorption performance of metal–organic frameworks (MOFs) is developed. It is shown that the graph isomorphic layer of this algorithm can directly learn the feature representation of materials from the connection of atoms in MOFs. Then, XGBoost can be used to predict the adsorption performance of MOFs based on feature representation. In this sense, it is not only possible to achieve end-to-end prediction directly from the structure of MOFs to adsorption performance but also to ensure the accuracy of prediction. The comparison between Grand Canonical Monte Carlo (GCMC) simulation and prediction supports the performance and effectiveness of the proposed algorithm.

中文翻译：

基于分子结构和分子特异性参数预测气体吸附的 XGBoost 算法

在本文中，开发了一种基于图同构网络（GIN）的改进的极端梯度提升（XGBoost）算法，用于预测金属有机骨架（MOF）的吸附性能。结果表明，该算法的图同构层可以直接从MOFs中的原子连接中学习材料的特征表示。然后，XGBoost 可用于基于特征表示预测 MOF 的吸附性能。从这个意义上说，不仅可以实现直接从MOFs的结构到吸附性能的端到端预测，而且可以保证预测的准确性。Grand Canonical Monte Carlo (GCMC) 模拟和预测之间的比较支持所提出算法的性能和有效性。

更新日期：2023-05-02

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