Collaborative Research: DMREF: Establishing a molecular interaction framework to design and predict modern polymer semiconductor assembly

Non-technical Description: Electronic materials based on plastics possess unique optoelectronic and processing properties that provide exceptional opportunities for large-area lighting applications, novel versatile solar-energy harvesting platforms, next-generation sensors, future computing options, and beyond. If the development of plastic electronics systems can be accelerated, step changes will be achieved.

For instance, new sustainable technologies may be produced that, e.g., integrate semi-transparent solar cells with greenhouses to yield a new class of sustainable, zero-energy, controlled-environment agriculture; assist with smart and controllable heat management for cars and office buildings; and/or allow the design of novel health-care devices. Despite extensive past efforts to further advance these materials and technology platforms, design- and processing- protocols remain based on trial-and-error methods, hampered by the highly intricate structure of plastic semiconductors, including complex structural dynamics.

The critical bottleneck is that structure-function relations cannot be understood and categorized with classic nomenclature and classic approaches. New polymer physics and multi-disciplinary ML/AI approaches are proposed to be introduced to accelerate materials development.

Technical Description: In this DMREF research, a framework will be delivered to expand polymer physics concepts. The framework will consider that state-of-the-art polymer semiconductors have an insoluble, complex electron donor-acceptor (D-A) backbone as well as large sidechains that provide solubility. The vision is to advance a knowledge platform toward the predictable and controlled self-assembly of multicomponent plastic semiconducting inks that lead to targeted device properties.

This will require the framework to accurately capture the molecular interactions and the complex secondary and tertiary structures of modern D-A semiconducting polymers and enable the development of descriptors that dictate and classify the assembly of these interesting macromolecules based on their intricate primary chemical molecular design. The project’s objective will be pursued by combining academic research in modeling self-assembly, polymer physics/thermal phase behavior, relaxation analysis, and molecular packing/assembly, with contributions by national laboratory partners in solution scattering (NIST) and theory/modeling (LANL), and targeted synthesis by numerous synthetic collaborators.

ML/AI analytics and methodologies will enhance experimental efficiency and knowledge inferences. This project, thus, can be expected to have broad implications in macromolecular science and technology in general. The project will also provide a highly interdisciplinary workforce trained in experimental methodologies, theory, simulations, and machine-learning techniques and a general community more aware of the benefits of the Materials Genome Initiative.

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.