Figure 3

Testing of the trained CNN on accurately generating spinodal structure with special self-connectivity and stochasticity. (A) an example of full CNN-based spinodal decomposition simulation; (B) extraction of two-phase spinodal structure (i.e., voxelization) from raw simulation result; number of (C) connected solid phases and (D) connected pore phases within the evolving spinodal structures throughout the spinodal decomposition process; (E) illustration of local pore size for a spinodal structure by calculating the local thickness; (F) pore size distribution for two spinodal structures generated by CNN and PFM, as well as a conventional stochastic porous structures as benchmark; (G) diffusion modeling result of concentration distribution for the three porous structures; (H) summary of geometrical characteristics and transport property of the three porous structures.