Chemically transferable electronic coarse graining for polythiophenes

Recent advances in machine-learning-based electronic coarse graining (ECG) methods have demonstrated the potential to enable electronic predictions in soft materials at mesoscopic length scales. However, previous ECG models have yet to confront the issue of chemical transferability. In this study, we develop chemically transferable ECG models for polythiophenes using graph neural networks. Our models are trained on a dataset that samples over the conformational space of random polythiophene sequences generated with 15 different monomer chemistries and three different degrees of polymerization. We systematically explore the impact of coarse-grained (CG) representation at multiple resolutions on ECG accuracy, highlighting the significance of preserving the C-beta coordinates in thiophene. We also find that integrating unique polymer sequences into training enhances model performance more efficiently than augmenting conformational sampling for sequences already in the training dataset. Moreover, our ECG models, developed initially for one property and one level of quantum chemical theory, can be efficiently transferred to related properties and higher levels of theory with minimal additional data. The chemically transferable ECG model introduced in this work will serve as a foundation model for new classes of chemically transferable ECG predictions across a broader chemical space.