Journal Supplementary Cover

Publication Details

Title: Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction

Authors: Mohamad A. Raja, Wonki Kim, Wonvin Kim, Su Hyun Lim, Seong Su Kim*

Journal: ACS Applied Materials & Interfaces
Citation: 2025, 17(13), 20125–20137

✨ Selected as Journal Supplementary Cover! ✨

Research Overview

Multifunctional structural batteries combine load-bearing capacity and energy storage, promising significant weight reduction and enhanced safety in aerospace applications. However, commercialization challenges remain due to vast design spaces and costly trial-and-error approaches.

Our Approach

This work accelerates the design of CF-based structural batteries impregnated with solid polymer electrolyte (SPE) using an experimentally validated framework:

• Finite Element Analysis based on computational micromechanics examines the CF/SPE interface
• Predictive modeling of effective material properties
• Bayesian-Optimized Artificial Neural Networks (ANN) forecast capacity under rapid degradation conditions

Key Insights

This research provides promising guidelines for optimizing multifunctional composites and advancing next-generation structural battery technology.

Access the Paper

📄 Read the full paper: DOI: 10.1021/acsami.4c19073

🔗 View the journal cover: ACS Applied Materials & Interfaces - Issue 13

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