Machine Learning Discovery of Multi-Functional Polyimides

Polyimides have been widely used in modern industries but it takes decades of experimental efforts to develop a successful product. Aiming to discover high-performance polyimides, we utilize computational methods of machine learning (ML) techniques and molecular dynamics (MD) simulations. We first build a dataset of more than 8 million hypothetical polyimides based on the polycondensation of existing dianhydride and diamine/diisocyanate molecules. Then we establish multiple ML models for thermal and mechanical properties of polyimides based on their experimentally reported values. The obtained ML models demonstrate excellent predictive performance and identify the key chemical substructures influencing the thermal and mechanical properties of polyimides. Applying the well-trained ML models, we obtain property predictions of the 8 million hypothetical polyimides. In such way, we explore the whole hypothetical dataset and identify 3 novel polyimides that have better combined properties than existing ones through Pareto frontier analysis. Furthermore, we validate the ML predictions through all-atom MD simulations and examine their experimental synthesizability. This study discovers novel polyimides and guides the further experimental synthesis of innovative polyimides.