Multi-fidelity machine learning models for structure–property mapping of organic electronics

Machine learning approaches have been used with significant success in constructing, curating, and exploring relationships between microstructure and property. However, one major limitation of these approaches is the need for a significant amount of training data consisting of microstructure–property pairs. Getting property values associated with a specific microstructure typically requires deploying a detailed physics simulator which becomes resource-intensive. While using a low(er) fidelity property quantifier can offset the cost of creating the training dataset, there is a trade-off in terms of accuracy/fidelity of the estimated property. Here, we leverage the availability of low- and high- fidelity property simulators to construct a multi-fidelity mapping from microstructure to property using deep convolutional neural networks. Starting with a large dataset of morphologies representing the active layer of organic photovoltaic devices, we assimilate data from a rapid graph-based low-fidelity characterization of the morphology with limited data from a high fidelity excitonic drift-diffusion detailed physics simulator. We show that our method provides significant computational savings while maintaining competitive performance. This work can be easily extended to other applications, and we envision it as a basis for accelerated material quantification and discovery. [Display omitted] •Formulated a deep learning framework that assimilates data from low- and high-fidelity simulators to predict microstructure property.•While high fidelity metric takes hours to evaluate, the low fidelity metric can be computed in seconds.•Approach shows significant reduction in data requirement compared to a single fidelity model.