| 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. |