Polymer Informatics for Energy Applications

Polymers play a critical role in advancing energy technologies due to their unique properties and broad applicability in mass and energy transport, particularly in gas separation and thermal management. This thesis presents advancements in polymer informatics that integrate computational simulations, predictive machine learning (ML), and generative design to accelerate the discovery of high-performance polymers for energy applications. We first introduce a graph-augmented, imbalanced ML framework for predicting polymer gas permeability and identifying promising polymers that surpass conventional performance limits. We then extend this approach to ladder polymers—materials with superior separation performance but limited prior exploration—by developing PolyLand, an ML-powered platform that combines predictive modeling and three generative strategies: template-based design, graph diffusion transformers, and optimization-aware large language models. The selected ladder-like candidates are validated via molecular simulations and structure-property analysis. Shifting focus to thermal transport, we explore polymer blends using high-throughput molecular dynamics simulations coupled with an active learning framework. This work reveals how inter- and intra-molecular interactions influence thermal conductivity, identifying blends with improved performance. To support broader discovery efforts, we introduce POINT², a benchmark suite combining predictive accuracy, uncertainty quantification, interpretability, and synthesizability. POINT² features diverse ML models and representations trained on labeled datasets across multiple polymer properties, including density, glass transition temperature, melting temperature, fractional free volumn, gas permeability, and thermal conductivity. Together, these contributions demonstrate how data-driven methods can accelerate polymer discovery, enabling more efficient and targeted design of materials for energy-related applications.