Enhancing high-throughput battery materials discovery and design with artificial intelligence
Qian Zhao, Bing He, Yurong Ren et al.
2026 · eScience
Battery material discovery and design face sophisticated multi-objective, multi-scale optimization challenges in high-dimensional variable spaces, a core bottleneck limiting advanced energy storage device upgrading. Prompted by the Materials Genome Initiative (MGI), high-throughput computation (HTC) has become a powerful approach to address these challenges via high-throughput automated computational workflows and hierarchical screening technologies. With the continuous accumulation of battery materials data and advances in artificial intelligence (AI) development, HTC is well-positioned to i…
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