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Meeting Materials Science & Technology 2020
Symposium Materials Informatics and Modeling for 21st Century Ceramics Research
Presentation Title Coarse-grained Equation-free Time Evolution of Microstructures with Deep Learning
Author(s) Fei Zhou, Ming Tang
On-Site Speaker (Planned) Fei Zhou
Abstract Scope Microstructures play important roles in many advanced materials, with strong, often decisive, effects on their mechanical and functional properties. For the purpose of modeling microstructure time evolution, direct molecular dynamics (MD) simulation is hindered by very demanding length and time scales involved, while coarse-graining approaches such as the phase field method (PFM) require strong prior assumptions such as the functional form the differential equations. We propose deep neural networks (NN) as a new, systematic paradigm for coarse-grained dynamics of microstructure solidification and solid-solid transitions. Through select case studies for both 2D and 3D microstructure evolution, we show that NN can effectively reach time & length scales beyond current methods, with huge speed-up, systematically improvable accuracy, general applicability, and quick turn-around.
Proceedings Inclusion? Undecided


Coarse-grained Equation-free Time Evolution of Microstructures with Deep Learning
Predicting Stress Hotspots in Polycrystalline Materials from Microstructural Features Using Deep Learning
Using Materials Informatics to Quantify Complex Correlations Linking Structure, Properties and Processing

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