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

该项目将开发塑料光学材料，以制造用于长波红外(LWIR)热成像系统的透镜、窗口和光学元件。红外热成像和光学技术在国防和军事领域至关重要，而红外热成像和检测在消费电子、交通、医疗成像、安全和机器人领域的应用潜力也已为人所知。然而，红外摄像机和放大器探测器的高昂成本阻碍了这些系统在消费市场的广泛使用。在这些成像系统中，绝大多数都需要昂贵的无机半导体来制造长波红外光学元件。重要的是，在这些努力中使用的常见材料之一，锗，已被确定为美国的关键矿物，如果缺乏，将深刻影响美国的国防能力。因此，开发用于长波红外塑料光学元件的新型廉价且可注塑的聚合物将是降低长波红外相机成本和确保美国国家安全的重大进步。为了解决这一具有挑战性的问题，这个跨学科的项目是与空军研究实验室(AFRL)的科学家合作发起的，它将利用广泛的计算工具和机器学习能力来加速材料发现。因此，该项目与促进全球竞争力的材料基因组倡议(MGI)密切相关。该项目旨在生产具有高LWIR透明度、高折射率和熔体可加工性的光学聚合物。这项研究将探索利用元素硫和液体硫介质来制备用于长波红外热成像的高折射率(RI，或n)、长波红外透明聚合物光学元件。该项目将由计算化学工具指导，以加快设计和合成工作。密度泛函理论(DFT)和分子动力学(MD)将被用来模拟共聚单体和杂化硫共聚物的红外光谱，并从数百万已知分子的数据库中快速筛选出具有高LWIR透明度的候选分子。此外，将开发机器学习工具，以快速创建新的候选材料库。量子团簇平衡(QCE)计算将与密度泛函(DFT)和分子动力学(MD)一起被用来量化液态硫的复杂的、动态的性质，并确定溶解度参数，以扩大可混溶/可溶有机共单体的范围。将开发ML工具来整合所有这些工作，并创建用于快速发现LWIR聚合物的通用计算程序包。计算工作将与新型硫系杂化聚合物的(宏观)分子合成、聚合物加工和光学表征密切相关。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.