De Novo Design of Polymers with Specified Properties Using Reinforcement Learning

Designing polymers with specified properties is crucial for industries such as aerospace, automotive, and construction, where high yield strength is necessary for stability and performance. Traditionally, the design of polymers has relied on decades of trial-and-error experiments that are time-consuming and inefficient. While recent advancements in computational methods have emerged as promising tools for polymer design, they predominantly focus on property prediction or unbiased polymer generation and do not fully progress toward the tailored design of novel polymer structures that meet specific performance criteria. To accelerate the exploration of new high-performance polymers, we proposed Reinforcement Learning for Polymer Generation (RLPolyG), an integrated goal-oriented exploration workflow for de novo polymer design with specified properties. This framework employs a forward model for predicting polymer properties and an inverse model optimized via reinforcement learning to generate polymers with specific yield strength. Our forward model achieved an R² of 0.84 in predicting yield strength, enabling the inverse model to generate 4991 novel polymer candidates, resulting in a significant 45.20% improvement in average yield strength. We further screened these candidates based on synthetic accessibility (SA) scores and degradability, identifying 3099 polymers with excellent feasibility for synthesis and degradation performance. Finally, we validated the nine top-performing polymers through molecular dynamics (MD) simulations, which showed an average related error of 14.64% between the predicted and MD-validated values. This work demonstrates the potential of using reinforcement learning to transform polymer design, providing a systematic and efficient pathway to explore the vast polymer space and accelerate the discovery of materials tailored to meet specific industrial needs.