ProgramMaster Logo
Conference Tools for 2022 TMS Annual Meeting & Exhibition
Register as a New User
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting 2022 TMS Annual Meeting & Exhibition
Symposium Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials
Presentation Title Machine Learning Based Hierarchical Multi-scale Modeling of Mechanical Deformation for Metal-matrix-nano-composites
Author(s) Md Shahrier Hasan, Wenwu Xu
On-Site Speaker (Planned) Md Shahrier Hasan
Abstract Scope The mechanical properties of Metal-Matrix-Nano-Composites (MMNC) have demonstrated significant enhancement with the presence of a small fraction of nano-inclusions. To understand the effect of nano-inclusions on the macro-scale or continuum level properties of MMNCs, a multi-scale modeling is necessary, one that can pass the atomistic mechanism-dependent information to the continuum calculations. In this work, a novel Machine Learning (ML) enabled hierarchical multi-scale modeling was developed by coupling the atomistic Molecular Dynamics (MD) simulations with the macro-scale Finite Element Method (FEM) to understand and make predictions on how the nano-inclusions affect the macroscopic properties of MMNCs. At first, MD simulations at various loading conditions and nano-inclusion structures were conducted to generate constitutive data which are subsequently used to train various ML classification and regression models. These ML models are implemented as a constitutive material model in the macro-scale FEM, achieving the multi-scale modeling of mechanical deformation of MMNCs.
Proceedings Inclusion? Undecided
Keywords Machine Learning, Modeling and Simulation, Mechanical Properties


Accelerated Discovery of Multi-phase Refractory Alloys through Machine Learning Surrogate Models of CALPHAD
Autonomous X-ray Scattering for the Study of Non-equilibrium Self-assembly
Designing Nano-architectured Materials with a Machine-learning Augmented Framework
Discovery of Nanocomposite Phase Change Memory Materials via Closed-loop Autonomous Combinatorial Experimentation
Intelligent Design of Additively Manufactured Architected Materials
Machine Learning Based Hierarchical Multi-scale Modeling of Mechanical Deformation for Metal-matrix-nano-composites
Volumetric Nanoscale Imaging of DNA-assembled Nanoparticle Superlattices
“Big Data” Characterization of Material Properties and High Temperature Kinetics

Questions about ProgramMaster? Contact