Citation: Jiali CHEN, Guoxiang ZHAO, Yayu YAN, Wanting XIA, Qiaohong LI, Jian ZHANG. Machine learning exploring the adsorption of electronic gases on zeolite molecular sieves[J]. Chinese Journal of Inorganic Chemistry, ;2025, 41(1): 155-164. doi: 10.11862/CJIC.20240408 shu

Machine learning exploring the adsorption of electronic gases on zeolite molecular sieves

Jiali CHEN ^{1, 2, 3, #} ,
Guoxiang ZHAO ^{1, #} ,
Yayu YAN ^{1, 2, 3} ,
Wanting XIA ^{1, 2, 3} ,
Qiaohong LI ^{1, 3,,} ,
Jian ZHANG ^{1, 3,,}

1.
State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China
2.
College of Chemistry, Fuzhou University, Fuzhou 350108, China
3.
Fujian College, University of Chinese Academy of Sciences, Fuzhou 350002, China

Corresponding author: Qiaohong LI, lqh2382@fjirsm.ac.cn Jian ZHANG, zhj@fjirsm.ac.cn
Received Date: 14 November 2024
Revised Date: 23 December 2024

Figures(6)

Using machine learning for high-throughput screening is a new material screening method. The gas adsorption on zeolite molecular sieves was studied using the grand canonical Monte Carlo (GCMC) simulation and machine learning methods. The GCMC simulation method was used to calculate the absolute adsorption capacity of 12 types of electron gases on 240 varieties of silica zeolite molecular sieves. In comparison, the Zeo++ program was employed to analyze 17 types of structural characteristics of these zeolite molecular sieves. On this basis, multiple linear regression and random forest regression were established to predict the adsorption capacity of zeolite molecular sieves for electronic gases. Through correlation analysis and model performance evaluation, the impact degree of different structural characteristics on gas adsorption capacity was revealed, and the stability and prediction accuracy of the model were discussed.
- electronic specialty gas,
- zeolite molecular sieve,
- machine learning,
- grand canonical Monte Carlo simulation
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