AI-driven Inverse Design of High-performance Viscosity Modifiers

Zhi-Wei Wang; Ze-Xuan Pu; Li-Feng Xu; Shi-Chao Li; Jian Zhang; Jian Jiang

doi:10.1007/s10118-025-3404-9

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AI-driven Inverse Design of High-performance Viscosity Modifiers

RAPID COMMUNICATION | Updated：2025-09-26

- AI-driven Inverse Design of High-performance Viscosity Modifiers
- Chinese Journal of Polymer Science Vol. 43, Issue 10, Pages: 1700-1706(2025)
- Affiliations：
  
  a.CNOOC Research Institute Co., Ltd., Beijing 100028, China
  b.State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 102209, China
  c.Beijing National Laboratory for Molecular Sciences, State Key Laboratory of Polymer Physics and Chemistry, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
  d.University of Chinese Academy of Sciences, Beijing 100049, China
- Author bio：
  
  zhangjian@cnooc.com.cn (J.Z.)
  jiangj@iccas.ac.cn (J.J.)
- Funds：
- DOI：10.1007/s10118-025-3404-9
  CLC：
- Received：25 June 2025，
  
  Accepted：25 June 2025，
  
  Online First：10 September 2025，
  
  Published：05 October 2025
- Accepted：
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Wang, Z. W.; Pu, Z. X.; Xu, L. F.; Li, S. C.; Zhang, J.; Jiang, J. AI-driven inverse design of high-performance viscosity modifiers. Chinese J. Polym. Sci. 2025, 43, 1700–1706

Zhi-Wei Wang, Ze-Xuan Pu, Li-Feng Xu, et al. AI-driven Inverse Design of High-performance Viscosity Modifiers[J]. Chinese Journal of Polymer Science, 2025, 43(10): 1700-1706.
Wang, Z. W.; Pu, Z. X.; Xu, L. F.; Li, S. C.; Zhang, J.; Jiang, J. AI-driven inverse design of high-performance viscosity modifiers. Chinese J. Polym. Sci. 2025, 43, 1700–1706 DOI： 10.1007/s10118-025-3404-9.

Zhi-Wei Wang, Ze-Xuan Pu, Li-Feng Xu, et al. AI-driven Inverse Design of High-performance Viscosity Modifiers[J]. Chinese Journal of Polymer Science, 2025, 43(10): 1700-1706. DOI： 10.1007/s10118-025-3404-9.

摘要

An AI-driven inverse design framework identifies optimal polymer structures for enhanced oil recovery by mapping target viscosity to molecular features

achieving a 12% improvement in viscosity through simulation-guided optimization of polymer topology and functionality.

Abstract

Polymer flooding is a widely used technique in enhanced oil recovery (EOR)

but its effectiveness is often hindered by the poor viscosity retention of conventional polymers like hydrolyzed polyacrylamide (HPAM) under high-salinity conditions. Although recent advances in molecular engineering have concentrated on modifying polymer architecture and functional groups to address this issue

the complex interplay among polymer topology

charge distribution and hydrophilic-hydrophobic balance renders rational molecular design challenging. In this work

we present an AI-driven inverse design framework that directly maps target viscosity performance back to optimal molecular structures. Guided by practical molecular design strategies

the topological features (grafting density

side-chain length) and functional group-related features (copolymerization ratio

hydrophilic-hydrophobic balance) are encoded into a multidimensional design space. By integrating dissipative particle dynamics simulations with particle swarm algorithm

the framework efficiently explores the design space and identifies non-intuitive

high-performing polymer structure. The optimized polymer achieves a 12% enhancement in viscosity

attributed to the synergistic effect of electrostatic chain extension and hydrophobic aggregation. This study demonstrates the promise of AI-guided inverse design for developing next-generation EOR polymers and provides a generalizable approach for the discovery of functional soft materials.

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references

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