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  • Open Access
  • Mini-Review

Recent Progress, Challenges, and Future Perspectives in AI-Driven Discovery and Optimization of Advanced Energy Materials

  • Dilshod Nematov 1,2,*,   
  • Iskandar Raufov 1,   
  • Sherali Murodzoda 1,   
  • Saidjafar Murodzoda 1,   
  • Sakhidod Sattorzoda 1,   
  • Anushervon Ashurov 1

Received: 17 Jun 2026 | Revised: 27 Jun 2026 | Accepted: 29 Jun 2026 | Published: 14 Jul 2026

Abstract

The rapid transition toward sustainable energy systems has created an urgent demand for advanced functional materials capable of improving energy conversion, storage, and utilization technologies. Artificial intelligence has emerged as a key enabling technology for accelerating materials discovery through data-driven prediction, inverse design, autonomous experimentation, and intelligent decision-making. This mini-review critically examines recent advances in AI-assisted discovery and optimization of advanced energy materials, covering machine learning, deep learning, graph neural networks, transformer models, generative AI, large language models, and self-driving laboratories. Unlike previous reviews that primarily focus on individual AI methodologies or specific classes of energy materials, this work provides an integrated assessment of recent developments across the entire AI-driven materials discovery workflow, encompassing data infrastructures, predictive modelling, generative design, autonomous experimentation, and intelligent closed-loop optimization. Representative studies demonstrate that AI-assisted optimization has reduced battery fast-charging optimization time from approximately 500 days to 16 days and achieved prediction accuracies of up to R2 = 0.88 in virtual materials screening. Representative studies further demonstrate that recent generative AI models have produced more than twice as many stable novel materials while generating structures over ten times closer to DFT ground-state configurations, whereas AI-assisted image analysis has achieved automated materials characterisation with segmentation accuracies exceeding 91%. These advances demonstrate the growing practical value of artificial intelligence across batteries, photovoltaics, electrocatalysis, hydrogen technologies, and other sustainable energy applications. This mini-review further discusses recent progress in open materials databases, autonomous experimentation, foundation models, and AI-enabled research platforms that are reshaping modern materials development. Critical evaluation of the available literature indicates that data quality, model generalization, interpretability, computational cost, and experimental validation remain the principal barriers to broader implementation of AI in materials research. Based on the literature synthesized in this mini-review, future advances are expected to depend on the effective integration of generative AI, foundation models, physics-informed learning, and autonomous experimentation within intelligent closed-loop materials discovery ecosystems.

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Nematov, D.; Raufov, I.; Murodzoda, S.; Murodzoda, S.; Sattorzoda, S.; Ashurov, A. Recent Progress, Challenges, and Future Perspectives in AI-Driven Discovery and Optimization of Advanced Energy Materials. AI for Energy and Environment 2025, 1 (1), 3.
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