|About this Abstract
||2020 TMS Annual Meeting & Exhibition
||Algorithm Development in Materials Science and Engineering
||Designing High-strength Carbon-nanotube Polymer Composites using Machine Learning Algorithms Integrated with Molecular Dynamics Simulations
||Aowabin Rahman, Prathamesh Deshpande, Matthew Radue, Michael Czabaj, S Gowtham, Susanta Ghosh, Gregory Odegard, Ashley Spear
|On-Site Speaker (Planned)
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 through a polymeric resin, attributed to low CNT/polymer interfacial strength. We present a modeling framework that uses a machine-learning (ML) algorithm in conjunction with molecular-dynamics (MD) simulations to make design modifications at the CNT/polymer interface for improving the strength of CNT composites. The proposed framework uses a modular approach consisting of: (i) a methodology to insert reactive groups at the CNT-polymer interface and perform MD simulations of CNT pullout, (ii) an ML model to map effects of functionalization on MD model configuration (process to structure), and (iii) an ML model to predict pullout force from the functionalized CNT-composite model output (structure to property). The framework thus enables fundamental exploration of design space to develop high-strength CNT composites.
||Planned: Supplemental Proceedings volume