Reorganization Energy Predictions with Graph Neural Networks Informed by Low-Cost Conformers.

ABSTRACT: A critical bottleneck for the design of high-conductivity organic materials is finding molecules with low reorganization energy. To enable high-throughput virtual screening campaigns for many types of organic electronic materials, a fast reorganization energy prediction method compared to density functional theory is needed. However, the development of low-cost machine-learning-based models for calculating the reorganization energy has proven to be challenging. In this paper, we combine a 3D graph-based neural network (GNN) recently benchmarked for drug design applications, ChIRo, with low-cost conformational features for reorganization energy predictions. By comparing the performance of ChIRo to another 3D GNN, SchNet, we find evidence that the bond-invariant property of ChIRo enables the

SUBMITTER: Li CH 

PROVIDER: S-EPMC10848248 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature