DMREF: Data Driven Discovery of Conjugated Polyelectrolytes for Neuromorphic Computing

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DMREF: Data Driven Discovery of Conjugated Polyelectrolytes for Neuromorphic ComputingNon-Technical Description: As a potentially disruptive technology, neuromorphic computing breaks away from the current performance-limiting conventional computer architectures (i.e. von Neumann paradigm) by developing biologically inspired computational devices with artificial intelligence capabilities. Organic electronic materials have recently emerged as attractive alternatives to inorganic counterparts in neuromorphic computing owing to their low-energy switching, excellent tunability, low fabrication costs, and biocompatibility. In this project, we will establish a collaborative, multidisciplinary and data-centric research program to accelerate the discovery of novel conjugated polyelectrolytes (CPEs) with chemical structures tailored for the demands of neuromorphic computing. The project will bear direct impact on applications ranging from neuromorphic computing, to energy generation (photovoltaic and thermoelectric materials), sensing, robotics, and pathogen mitigation. The project will also provide cutting-edge educational and training opportunities to students and postdoctoral fellows who will gain valuable experience in data science, materials informatics, and data driven material research. The PIs are fully committed to broadening participation and enhancing diversity in materials research and education by strengthening opportunities for underrepresented groups in STEM fields. Exposing the underrepresented groups to data centric research and education is a key component of the project. A set of courses will be developed at various levels in both participating institutions to prepare the students and postdoctoral fellows for the proposed research. The PIs will reach out to local high schools and community colleges through targeted recruitment, workshops, and summer camps for high school teachers.Technical Description: The proposed research will significantly accelerate the discovery of CPE materials specifically designed for neuromorphic computing. The research effort integrates high-throughput computation, machine learning, multiscale modeling, chemical synthesis, and materials and device characterization executed in a "closed loop" manner. The team will construct the first comprehensive database dedicated to CPEs, which includes a collection of structural, elastic, vibrational, electronic, dielectric, and energetic properties for over ten thousand CPEs. Based on the CPE database, the team will explore the correlations between the materials properties and formulate a set of molecular design rules. Materials characterization will be performed to validate the design rules and once validated, they will provide guidance for predictions of promising CPEs. Based on the predictions, the team will synthesize the most promising CPEs and examine their performance in the neuromorphic devices. The project has three deliverables: (1) The first comprehensive CPE database. (2) A fundamental understanding on how the backbone structure and adjacent electrostatic forces control CPE properties and a set of design rules for accelerated materials discovery. (3) A set of highly optimized and promising CPE structures for neuromorphic and optoelectronic applications. Successful completion of the project not only impacts neuromorphic computing, but also research areas, such as photovoltaics, light-emitting diodes, thermoelectrics, sensors and robotics. The potential to affect transformative breakthroughs in the rational design of neuromorphic and organic electronic materials makes a compelling case for the project.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.