A team of collaborators from the University of North Carolina at Chapel Hill (UNC) and Carnegie Mellon University (CMU) has won Phase 1 of the NSIN-sponsored AFRL Active AI Planners for Chemistry/Materials Optimization and Discovery Grand Challenge (AFRL Grand Challenge #2). The challenge includes the potential of a $500,000 contract, awarded in four development phases over the next nine months, to develop a machine learning-artificial intelligence system that can help researchers quickly find appropriate conditions for optimizing and discovering new synthetic compounds using multi-system approaches.
The team, led by Professors Frank Leibfarth of UNC and Olexandr Isayev of CMU, pitched a reinforcement learning strategy with an iterative computational and experimental approach as part of the grand challenge.
Each of the four semi-finalist teams impressed the judges, but the innovative approach taken by the UNC/CMU team put them over the edge. Regarding the team, Dr. Luke Baldwin, Research Chemist and Project Manager with AFRL, shared, “The UNC/CMU team will help build a general data science approach to push polymeric materials research towards properties-based objective functions. We are excited about bringing together advanced manufacturing with the aid of continuous flow chemistry, automation, active machine learning and reinforcement learning algorithms. Ultimately, AFRL is excited about the possibility of extending these strategies to new problem areas to accelerate chemistry and materials research across the defense sector.”
NSIN is a government program office within the Office of the Secretary of Defense for Research and Engineering (OSD(R&E)) that collaborates with major universities and the venture community to develop solutions that drive national security innovation. We operate two portfolios of programs and services: Talent and Venture. Together, these portfolios form a pipeline of activities and solutions that accelerate the pace of defense innovation.