Key Takeaways & Executive Findings
- •• Machine learning accelerates the discovery of high-performance cathode, anode, and electrolyte materials for solid-state batteries by mining extensive material databases. • ML algorithms accurately predict key battery management system indicators, including state of charge, state of health, remaining useful life, and capacity. • The review identifies critical challenges such as data quality issues and poor code portability, proposing solutions for future research. • AI-driven approaches significantly shorten the research and development cycle for solid-state batteries, overcoming traditional trial-and-error inefficiencies.
Abstract
Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy, high safety, and high environmental adaptability. However, the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment, rendering performance prediction arduous and delaying large-scale industrialization. Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction. This review will systematically examine how the latest progress in using machine learning (ML) algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode, anode, and electrolyte materials suitable for solid-state batteries. Furthermore, the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed, among which are state of charge, state of health, remaining useful life, and battery capacity. Finally, we will summarize the main challenges encountered in the current research, such as data quality issues and poor code portability, and propose possible solutions and development paths. These will provide clear guidance for future research and technological reiteration.
1. Introduction
In the context of the global community actively seeking sustainable energy solutions [1–3], transformation of the energy structure is of decisive significance for alleviating the energy crisis, reducing environmental pollution, and promoting sustainable economic development. As a crucial force in achieving low-carbon emissions reduction and addressing energy challenges, electric vehicles are experiencing rapid development at an unprecedented pace [4–6]. Solid-state batteries, with advantages in energy density, safety, and cycle life over traditional lithium-ion batteries, have become a focus of next-generation energy storage devices [7–12]. Although solid-state batteries hold immense potential, their complex chemical environments necessitate novel approaches to overcome material and performance challenges. Meanwhile, the development momentum of artificial intelligence (AI) technology has shown an explosive growth trend and has already played a powerful driving role in various different fields [13–20]. Machine learning (ML), deep learning (DL), etc., as the core branches of AI, possess the astonishing ability to process massive amounts of data [21]. Through complex algorithm models, they can excavate the hidden complex patterns, laws, and trends behind the data and, based on these findings, make highly accurate predictions and intelligent decisions, thus enabling the screening of solid-state battery materials and the prediction of their performance.
Although the future of solid-state batteries is promising, there are still many challenges in their journey toward practical applications. In the aspect of material screening, identifying an ideal combination of solid-state electrolytes (SSEs) and electrode materials demands a comprehensive and profound exploration and screening of a voluminous and diverse material system [22]. Each step in experimental design, sample preparation, and performance characterization requires significant time and financial resources. Traditional trial-and-error methods are often slow and inefficient, hindering their ability to keep pace with rapidly evolving technologies [23]. However, the application of AI and ML models has constructed an efficient and precise strategic system for screening SSEs and electrode materials, which significantly enhances the efficiency and success rate of material screening and substantially shortens the research and development cycle [24–26]. For example, Ahmad et al. [27] computationally screened over 12,000 inorganic solids for next-generation lithium-metal anode batteries. Using a ML model, they predicted new SSEs’ mechanical properties, and cross-validation verified the model’s robustness. In another study, Hajibabaei et al. [28] employed an extensible sparse Gaussian process regression form and replicated the experimental melting temperature and glass-crystallization te
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Sheng Wang, Jincheng Liu, Xiaopan Song, Huajian Xu, Yang Gu, Junyu Fan, Bin Sun, Linwei Yu (2025). Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation. Nano-Micro Letters. https://doi.org/10.1007/s40820-025-01797-y
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Frequently Asked Questions
What is the role of artificial intelligence in solid-state battery development?
AI, particularly machine learning and deep learning, accelerates the screening of materials and prediction of performance, significantly reducing time and cost compared to traditional trial-and-error methods.
Which battery management system parameters can be predicted using machine learning?
Machine learning can accurately estimate and predict state of charge, state of health, remaining useful life, and battery capacity.
What are the main challenges in applying AI to solid-state batteries?
Key challenges include data quality issues and poor code portability, which hinder the reproducibility and scalability of AI models.
How does machine learning improve material screening for solid-state batteries?
ML algorithms mine extensive material databases to identify high-performance cathode, anode, and electrolyte materials, enhancing efficiency and success rate while shortening R&D cycles.
What is the significance of this review for future research?
The review provides a systematic examination of ML applications, highlights current challenges, and proposes solutions and development paths, offering clear guidance for future research and technological advancement.
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