AI-Guided High-Throughput and Uncertainty-Aware Discovery of Stable Na-Ion Cathodes

The rapid advancement of autonomous materials research requires data-efficient frameworks that can integrate artificial intelligence (AI) with experimental knowledge and human intuition. In the context of sodium-ion batteries (SIBs), where compositional and processing complexity spans an immense design space, conventional high-throughput experiments still face bottlenecks in data utilization and decision-making efficiency. To address this challenge, we developed an AI-guided, uncertainty-aware workflow that couples high-throughput synthesis and characterization [1-3] with machine learning (ML) surrogate modeling and probabilistic deep learning. Specifically, we established Monte Carlo (MC) Dropout–based multilayer perceptron (MLP) models to predict key electrochemical metrics such as initial capacity and long-term retention from high-dimensional descriptors encompassing composition, structure, and processing features. The MC-MLP provides not only accurate property predictions but also calibrated uncertainty estimates that quantify model confidence and guide active learning. These models are integrated with gradient boosting regression (GBR) surrogate mappings that link intermediate feature spaces to improve physical interpretability and reduce data sparsity. Tail-focused loss functions and temperature-balanced sampling further enhance sensitivity toward high-performance outliers, accelerating the identification of promising chemistries within limited datasets. By combining these ML strategies with automated synthesis and characterization workflows, we demonstrate an iterative human-AI loop: experimental data continuously refines the surrogate landscape, while uncertainty-driven candidate selection prioritizes new experiments. This approach achieved >3× improvement in data efficiency and enabled Pareto-optimized discovery of Na–Ni–Mn–based layered cathodes with enhanced air stability and electrochemical performance. Moreover, the framework is transferable across chemical systems, synthesis conditions, and multidimensional battery characterization data, enabling mechanistic understanding and accelerated discovery of novel electrode materials. Overall, this work highlights how integrating MC-Dropout deep learning, surrogate modeling, and domain knowledge from real lab-generated data can transform high-throughput experimentation into a self-improving, autonomous discovery framework—paving the way toward truly intelligent, data-efficient materials research. References [1] Jia, Shipeng, et al. "High-throughput design of Na–Fe–Mn–O cathodes for Na-ion batteries." Journal of Materials Chemistry A 10.1 (2022): 251-265. [2] Jia, Shipeng, et al. "Chemical speed dating: the impact of 52 dopants in Na–Mn–O cathodes." Chemistry of Materials 34.24 (2022): 11047-11061. [3] Jia, Shipeng, et al. "Stabilization of Na‐Ion Cathode Surfaces: Combinatorial Experiments with Insights from Machine Learning Models." Advanced Energy and Sustainability Research (2024): 2400051