Cognitive Geometry Adaptive Optimization (CGAO): A Perception-Driven Framework for Intelligent Nanoparticle Design and Electrochemical Enhancement

Abstract

Nanoparticle morphology critically influences electrochemical behavior, yet existing optimization techniques often fail to balance structural precision, charge transport efficiency, and synthesis constraints simultaneously. To address this limitation, this paper introduces a novel Cognitive Geometry Adaptive Optimization (CGAO) framework that unifies geometric cognition, adaptive swarm learning, and multi-objective optimization for intelligent nanoparticle design. CGAO employs a dual-layer perception model that encodes morphological descriptors—such as aspect ratio, surface curvature, and topology symmetry—into a high-dimensional latent space, enabling accurate prediction of structure–property correlations. The optimization core integrates cognitive feedback adaptation, dynamically adjusting exploration–exploitation behavior based on geometry-perception metrics to converge toward optimal synthesis parameters. The proposed framework is validated through simulations and experimental data on metallic oxide and alloy nanoparticles, demonstrating up to 23.8% enhancement in specific capacitance, 19.4% improvement in charge transfer efficiency, and 28.7% reduction in energy loss compared to state-of-the-art algorithms such as PSO, HHO, and DE. Morphology characterization using SEM and AFM confirms the structural uniformity and surface optimization achieved by CGAO. The results establish CGAO as a robust, perception-aware metaheuristic that bridges computational intelligence and materials science, paving the way for autonomous discovery of high-performance electrochemical materials.