Literature review demonstrates the pivotal role of high-quality dataset construction in energy chemistry, indicating that overcoming data scarcity is vital for AI deployment.
Artificial Intelligence (AI) has become an increasingly important propellant for energy materials and energy chemistry research, such as accelerating advanced energy materials discovery, analyzing vast amounts of data from both experiments and computations, process optimization for materials syntheses, management and monitoring of energy storage devices such as lithium batteries, and algorithm-optimized grid load forecasting. Looking back at recent pioneering works of AI-driven energy chemistry research, constructing a dataset with both high quality and large quantity is almost the first step and largely determines the following success of training AI models and figuring out corresponding scientific issues.
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Yuan et al. (2025) studied this question.
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