DMREF: Computational Chemistry to Accelerate Development of Long Wave Infrared Polymers

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This project will develop plastic optical materials to create lenses, windows, and optical elements for long wave infrared (LWIR) thermal imaging systems. IR thermal imaging and optical technologies are critical across the defense and military sectors, while the potential of IR thermal imaging and detection for applications in consumer electronics, transportation, medical imaging, security and robotics have also been known for decades. However, the high cost of IR cameras & detectors has impeded widespread use of these systems in consumer markets. In the vast majority of these imaging systems, expensive, inorganic semiconductors are required for the fabrication of LWIR optical components. Importantly, one of the common materials used in these efforts, germanium, has been identified as a US critical mineral, a lack of which would profoundly impact US defense capabilities. Hence, the development of new inexpensive and moldable polymers for use as LWIR plastic optics would be a significant advance to lower the cost of LWIR cameras and ensure US national security. To address this challenging problem, this interdisciplinary project has been launched in collaboration with scientists at the Air Force Research Laboratory (AFRL), and it will harness a wide range of computational tools and machine learning capabilities to accelerate materials discovery. Thus, the project closely aligns with the Materials Genome Initiative for Global Competitiveness (MGI).This project aims to yield optical polymers with high LWIR transparency, high refractive index, and melt processability. The research will examine the use of elemental sulfur and liquid sulfur media to prepare high refractive index (RI, or n), LWIR transparent polymer optics for LWIR thermal imaging. The project will be directed by computational chemistry tools to accelerate design and synthesis efforts. Density functional theory (DFT) and molecular dynamics (MD) will be used in tandem to simulate infrared (IR) spectra of comonomers and hybrid sulfur copolymers and rapidly screen candidates with high LWIR transparency from databases of millions of known molecules. Furthermore, machine learning tools will be developed to rapidly create new candidate material libraries. Quantum cluster equilibrium (QCE) calculations, along with DFT and MD, will be exploited to quantify the complex, dynamic nature of liquid sulfur and to determine solubility parameters in order to expand the scope of miscible/soluble organic comonomers. ML tools will be developed to integrate all of these efforts and create a universal computational package for rapid LWIR polymers discovery. The computational work will be closely looped to (macro)molecular synthesis, polymer processing and optical characterization of new chalcogenide-based hybrid polymers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.