AI-driven Inverse Design of High-performance Viscosity Modifiers

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.