SBIR Phase I: AI Robotics-driven Material Discovery Platform

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to accelerate the development of new high-performance battery materials with an Artificial Intelligence (AI) robotics-driven material development platform. The platform uses machine learning and robotic high-throughput automation to accelerate effective experiment planning and minimize errors. It will potentially have a substantial positive impact on the commercialization of superior battery materials (projected to be a $14 B market by 2025), to support growth of electric vehicles and other sustainable transportation. This Small Business Innovation Research (SBIR) Phase I project aims to build a material development platform featuring a closed-loop machine learning and robotic high-throughput automation, and to develop a high-performance polymer electrolyte product for lithium batteries. The platform can potentially change how material innovation is performed and enable accelerated discovery of electrolytes and other battery materials. The platform’s workflow iterates the following: (1) initial electrolyte knowledge base collection; (2) machine-learning model training using the knowledge base; (3) new electrolyte prescription by the model; (4) parallelized experimental validation via high-throughput equipment; and (5) knowledge base updates. Phase I will help to (1) build key electrochemical and mechanical modules on the robotic system for electrolyte development, (2) improve machine learning models in terms of feasibility, flexibility, and the capability of optimizing multiple objective functions, and (3) develop the polymer electrolyte formulation in order to improve its three primary properties, including ionic conductivity, voltage stability, and mechanical modulus. It is anticipated that the platform will achieve high productivity and effectiveness, significantly improve electrolyte properties, and identify an electrolyte that meets commercialization system requirements.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.