The Artificial Intelligence Guided Search for New CHIPs Comonomers

Chalcogenide Hybrid Inorganic/Organic Polymers (CHIPs) have the potential to revolutionize infrared (IR) optics and create sustainable and recyclable devices. CHIPs combine elemental sulfur with organic comonomers via inverse vulcanization to create a high-sulfur content polymer, with a refractive index and IR transparency that rival state of the art inorganic solids, while preserving the processability and recyclability of plastic materials. However, the optimal comonomer for these applications remains unknown. This manuscript presents a gradient boosted tree model that determines which comonomers merit further consideration as high-performing CHIPs materials. After training models on previously calculated IR-absorption data we apply them to a set of 960,934 molecules from the GDB dataset and validate the predictions for both highly transparent molecules and a set of one thousand randomly selected molecules. Finally, we compare their optical properties in the gas phase and in an approximation of a CHIPs motif by replacing their double bonds with elemental sulfur rings.