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

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Modeling

Background:

• Hierarchical porous carbons exhibit complex pore topologies that hinder energy transport.
• Conventional research uses simplified models, lacking insight into pore evolution dynamics.
• 3D characterization of these materials remains a significant challenge.

Purpose of the Study:

• To develop an integrated R&D framework for predicting and designing hierarchical porous carbons.
• To couple bidirectional performance prediction with physics-driven 3D structural prediction.
• To enable target-oriented inverse optimization for material design.

Main Methods:

• Constructed a high-precision bidirectional mapping model using CatBoost and differential evolution (DE) for inverse optimization.
• Developed a synergistic algorithm combining Gaussian random fields (GRF) and Cahn-Hilliard (C─H) phase-field dynamics for 3D topology prediction.
• Integrated particle tracking to identify transport hotspots and calibrate a non-Darcy kinetic scaling law.

Main Results:

• Identified critical porosity thresholds for micro-, meso-, and macropores (0.15, 0.25, 0.35) for global connectivity at 0.46 total porosity.
• Discovered transport hotspots contributing 80% of total flux and highlighted mesopores' role in alleviating kinetic bottlenecks (n = 1.6357).
• Experimental validation confirmed accurate performance targets (3.2%-7.4% error) and high-fidelity restoration of morphologies.

Conclusions:

• Established a robust, physics-driven paradigm for intelligent, target-oriented 3D structural prediction and design of porous materials.
• Transitioned from empirical trial-and-error to predictive design for hierarchical porous carbons.
• Demonstrated the framework's capability to accurately predict and validate material performance based on 3D structure.