Physics‐Driven 3D Structural Prediction and Transport Kinetics of Porous Carbons via Random Field and Phase‐Field Evolution

Abstract

The complex pore topology of hierarchical porous carbons constrains energy transport, yet conventional research relies on homogenized scalar descriptions, treating pore evolution as a black box. To overcome 3D characterization limits, this study establishes an integrated R&D framework coupling bidirectional performance prediction with physics‐driven 3D structural prediction. A high‐precision bidirectional mapping model (R2 = 0.8595) was constructed using CatBoost and differential evolution (DE) to enable target‐oriented inverse optimization. In the structural dimension, we developed a synergistic algorithm combining Gaussian random fields (GRF) and Cahn–Hilliard (C─H) phase‐field dynamics to dynamically predict authentic 3D topologies by simulating interfacial energy‐driven pore evolution. Findings reveal that global connectivity is achieved at a total porosity of 0.46, with specific critical thresholds of 0.15, 0.25, and 0.35 for micro‐, meso‐, and macropores, respectively. By integrating particle tracking, the study identifies transport hotspots contributing 80% of the total flux and calibrates a non‐Darcy kinetic scaling law (exponent n = 1.6357), highlighting mesopores' role in alleviating kinetic bottlenecks. Experimental validation confirms that the inverse‐optimized conditions accurately meet performance targets (error 3.2%–7.4%), while the 3D structural prediction model achieves high‐fidelity restoration of experimental morphologies. This work provides a robust physics‐driven paradigm for transitioning from empirical trial‐and‐error to intelligent, target‐oriented 3D structural prediction and design.