Machine learning for adsorption-related parameters prediction of electronic specialty gases: DFT-based dataset construction and balanced data augmentation

Wu Zhikang; Wu Ying; Miao Guang; Chen Runze; Ma Lingjun; Xi Hongxia; Xiao Jing

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Machine learning for adsorption-related parameters prediction of electronic specialty gases: DFT-based dataset construction and balanced data augmentation

Updated：2026-05-14

- Machine learning for adsorption-related parameters prediction of electronic specialty gases: DFT-based dataset construction and balanced data augmentation
- Machine learning for adsorption-related parameters prediction of electronic specialty gases: DFT-based dataset construction and balanced data augmentation
- 中国化学工程学报（英文） 2026年90卷第2期页码：261-271
- Affiliations：
  
  School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China
  Peric Special Gases Co., Ltd., Handan 057550, China
- Author bio：
  
  Corresponding author. E-mail address: cejingxiao@scut.edu.cn (J. Xiao).
- Funds：
  
  the support from the National Natural Science Foundation of China(U24A20532;22278146);Guangdong Basic and Applied Basic Research Team Fund(2024B1515040016);Fundamental Research Funds for the Central Universities
- DOI：
  中图分类号：
- 收稿：2025-07-15，
  
  修回：2025-09-22，
  
  录用：2025-09-22，
  
  网络首发：2025-10-18，
  
  纸质出版：2026-02
- Accepted：
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Wu Zhikang, Wu Ying, Miao Guang, 等. Machine learning for adsorption-related parameters prediction of electronic specialty gases: DFT-based dataset construction and balanced data augmentation[J]. 中国化学工程学报（英文）, 2026,90(2):261-271.

Wu Zhikang, Wu Ying, Miao Guang, et al. Machine learning for adsorption-related parameters prediction of electronic specialty gases: DFT-based dataset construction and balanced data augmentation[J]. Chinese Journal of Chemical Engineering, 2026, 90(2): 261-271.
Wu Zhikang, Wu Ying, Miao Guang, 等. Machine learning for adsorption-related parameters prediction of electronic specialty gases: DFT-based dataset construction and balanced data augmentation[J]. 中国化学工程学报（英文）, 2026,90(2):261-271. DOI：

Wu Zhikang, Wu Ying, Miao Guang, et al. Machine learning for adsorption-related parameters prediction of electronic specialty gases: DFT-based dataset construction and balanced data augmentation[J]. Chinese Journal of Chemical Engineering, 2026, 90(2): 261-271. DOI：

摘要

Abstract

Electronic

specialty gases play vital roles in key chip manufacturing processes like lithography

etching

deposition and cleaning. While their ultra-high purity (≥99.999%) creates challenging separation requirements

insufficient physicochemical data has hindered adsorbent development. To bridge this gap

we constructed a multidimensional database covering 101 semiconductor-related molecules with 19 physical parameters

and developed a Bayesian regression-based collaborative prediction model demonstrating high accuracy (

=0.95—0.97) on test sets. We further constructed the balanced dataaugmented Transformer-based molecular property prediction (BD-TMPP) model to address the overfitting problem in small-sample learning. This model achieves the end-to-end prediction of molecular quadrupole moment (

= 0.99)

and polarizability (

= 0.98)

via

the capture of interatomic spatial correlations. Compared with traditional density functional theory calculations

the model achieves a five-orders-of-magnitude improvement in computational efficiency while maintaining accuracy

demonstrating a successful application of the "structure-property relationship" theory in chemical machine learning.

关键词

Keywords

references

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