ABSTRACT Artificial intelligence (AI) is revolutionizing sustainable materials science, yet a comprehensive and timely evaluation of the rapidly evolving AI techniques applied across the entire materials lifecycle remains lacking. This work reviews AI‐driven advances in sustainable materials, specifically focusing on battery materials, thermal management materials, energy conversion materials, and catalysts. The key patterns, capabilities, and limitations of AI are identified across three interconnected phases: sustainable materials design (leveraging predictive and generative models for accelerated discovery), green processing (integrating adaptive synthesis optimization and autonomous experimentation), and extending to lifecycle management (encompassing real‐time monitoring, predictive maintenance, and intelligent recycling). Then, the persistent challenges, including data sparsity, domain‐specific knowledge integration, and limited model generalizability, are investigated, followed by an exploration of emerging solutions such as federated learning for privacy‐preserving data sharing, physics‐informed neural networks for knowledge integration, and multimodal AI for cross‐modal knowledge transfer. Finally, the computational sustainability challenges of AI methods themselves are also discussed. This review highlights key bottlenecks impeding scalable adoption and discuss pathways for realizing the full potential of AI in sustainable materials development.
Xian et al. (Wed,) studied this question.
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