Designing High-strength Carbon-nanotube Polymer Composites Using Reinforcement Learning Algorithms Integrated with Molecular Dynamics Simulations

Carbon-nanotube (CNT)-based composites have great potential in modern aerospace applications requiring high-strength, lightweight structural materials. However, one factor that limits the potential of CNT composites is the inefficiency in load transfer between CNTs using a polymeric resin, arising due to low CNT/polymer interfacial strength. This talk presents a modeling framework that uses a reinforcement learning (RL) algorithm along with molecular dynamics (MD) simulations to make design modifications at the CNT/polymer interface for improving the interfacial strength of CNT/polymer composites. The proposed framework uses a modular approach consisting of: (i) reinforcement learning model to recommend design modifications, i.e. inserting reactive groups and dopants, to the CNT/polymer model; (ii) method for rapidly implementing the recommendations by modifying the MD model structure; and (iii) methodology to reduce computational time for performing MD simulations of CNT pullout after making these modifications. The proposed framework would enable fundamental exploration of design space to develop high-strength CNT-based composites and could potentially be extended or adapted for a more general integration of data-driven techniques with MD for design applications.