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

doi:10.1016/j.cjche.2025.09.012

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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
- Chinese Journal of Chemical Engineering Vol. 90, Issue 2, Pages: 261-271(2026)
- 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：
- DOI：10.1016/j.cjche.2025.09.012
  CLC：
- Received：15 July 2025，
  
  Revised：2025-09-22，
  
  Accepted：22 September 2025，
  
  Online First：18 October 2025，
  
  Published：2026-02
- Accepted：
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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：

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： 10.1016/j.cjche.2025.09.012.

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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