Artificial Intelligence and Machine Learning

Artificial Intelligence-Driven Development in Rechargeable Battery Materials: Progress, Challenges, Perspectives Literature

What the paper covers:

Comprehensive review of AI/ML methods applied across the battery materials lifecycle, including materials discovery, formulation/process optimization, and performance prediction.

Focus on electrodes, electrolytes, interfaces SEI/CEI, and full-cell performance, spanning Li-ion and beyond-Li systems

Major challenges:

Data scarcity, inconsistency, and bias, especially for degradation, interfaces, shelf life, and long-term cycling

Limited model transferability and interpretability across chemistries and operating conditions

Gap among materials discovery, manufacturing process, and cell performance

Automated Extraction Manual Construction

Computation Experiment

Assisted Characterization Novel Materials Discovery Properties Prediction

Robustness Interpretability Accuracy Generalizability

Supervised Learning

Application Workflow of AI

Unsupervised Learning Reinforcement Learning

Hold-out

Key progress:

ML models enable faster screening and design of cathodes, anodes, and electrolytes using composition-, structure-, and physics-informed descriptors

Growing integration of DFT ML and data-driven surrogate models to reduce computational and experimental cost

Early success in property prediction (voltage, stability, ionic conductivity, cycle life)

Future directions:

Development of foundation models, standardized datasets, and automated data extraction

Closed-loop workflows combining AI, simulation, and (semi-) autonomous experiments

Greater emphasis on explainable AI, uncertainty quantification, and real-world validation

Bootstrap Cross Validation

AI is transforming rechargeable battery materials development by enabling closed-loop workflows that integrate data, models, and experiments to accelerate discovery, optimization, and insight generation. Source: Hu et al., Advanced Functional Materials, 2025.

Extracted from the PDF; charts and images may read imperfectly.