Quantitative relationships between structure characteristics and mass transfer for chromatographic resin by combining digital material techniques and machine learning algorithms

Liang-Zhi Qiao, A-Kang Tong, Yu-Xin Liao, Shan-Jing Yao, Dong-Qiang Lin,
Quantitative relationships between structure characteristics and mass transfer for chromatographic resin by combining digital material techniques and machine learning algorithms,
Chemical Engineering Science,
Volume 318,
2025,
122227,
ISSN 0009-2509,
https://doi.org/10.1016/j.ces.2025.122227.
(https://www.sciencedirect.com/science/article/pii/S0009250925010486)
Abstract: Quantifying the relationship between structure characteristics and mass transfer is essential for understanding the chromatographic behavior and improving the performance of resins. Here, a digital method was proposed to accurately establish this relationship by combining digital material techniques and machine learning algorithms. Digital material techniques were used to generate a large amount of structures and offer abundant data of resin structures and mass transfer. Then, machine learning algorithms were applied to develop the quantitative relationship between structure characteristics and mass transfer. The results showed that the machine learning models achieved better predictive accuracy with a reduction of 84–93% in mean absolute error (MAE) and an improvement of 1.54–2.23 times in determination coefficients (R2) compared to common empirical formulas. Moreover, feature importance analysis revealed that pore throat plays a pivotal role on the mass transfer in resins, which usually is neglected in common empirical formulas. Inspired by the finding, a empirical formula was modified by replacing porosity with throat size. The modified formula showed an improved prediction ability with a MAE value of 0.09 and an R2 value of 0.89. The quantitative relationship established in this work would serves as a prerequisite screening tool to accelerate the resin design and development.
Keywords: Machine learning; Digital material; Chromatographic resin; Mass transfer; Structure–property relationship