A critical bottleneck for the design of high-conductivity organic materials is finding molecules with low reorganization energy. The development of low-cost machine-learning-based models for calculating the reorganization energy has proven to be challenging. Here we combine a graph-based neural network recently benchmarked for drug design applications, ChIRo, with low-cost conformational features and show the feasibility of reorganization energy predictions on the benchmark QM9 dataset without needing DFT geometries.
