November 13, 2023
Assistant Professor Mohammad Malakooti and Assistant Professor Krithika Manohar recently received a National Science Foundation (NSF) grant of nearly $600,000 to establish a design framework for soft multifunctional composites and investigate their failure under large deformations. By building comprehensive and interpretable machine learning models, the project will enable efficient and reliable synthesis of composites with targeted properties.
These functional composites, incorporating hybrid fillers of various morphologies and physical characteristics, present abundant opportunities for emerging technologies. Applications include self-powered sensors, wearable electronics and soft robotics. Composites of this nature, with both solid and liquid phase materials, offer unique advantages such as maintained mechanical flexibility and improved toughness and conductivity. However, their synthesis is complicated, time-consuming and costly due to the numerous variables and synthesis parameters involved. In addition, their mechanical load capacity and susceptibility to failure remain uncertain.
“This NSF grant provides exciting opportunities to advance our understanding of the mechanics of hybrid filler composites, explore the entire design space and unlock new possibilities while refining the synthesis process for sustainability”
To tackle these challenges, the researchers will use artificial intelligence to develop models that expedite the discovery of optimal parameters and identify factors contributing to failure. These models will then be validated through experimental studies and further improved. This research will result in a comprehensive data library for soft multifunctional materials and a universal framework for the efficient design of multiphase materials with engineered properties and failure characteristics. The award also supports developing a diverse workforce through outreach programs and creating free educational online content about advanced mechanics and artificial intelligence.