About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data informatics: Design of Structural Materials
|
Presentation Title |
Unsupervised ML to Bridge Molecular Dynamics and Phase field Simulations |
Author(s) |
Sukriti Manna, Henry Chan, Subramanian Sankaranarayanan |
On-Site Speaker (Planned) |
Sukriti Manna |
Abstract Scope |
A machine learning approach is demonstrated to bridge atomistic molecular dynamics and mesoscopic phase-field method. We use a representative grain growth simulation to demonstrate the efficacy of our approach. Our molecular dynamics simulation evaluates the materials properties such as grain boundary energy, grain boundary width, and grain boundary mobility. An unsupervised ML is used to seamlessly feed materials properties from MD to evaluate our phase-field model parameters and back-mapping atoms on to the phase-field model. An efficient information exchange between atomistic molecular dynamics and mesoscopic phase-field method enables us to leverage the best attributes of molecular dynamics and phase-field simulations for mesoscale simulations. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, ICME |