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Solid-state batteries (SSBs) have emerged as promising candidates for the next generation of energy storage systems due to their high energy density and enhanced safety. In recent years, machine learning has become a transformative tool in battery research, enabling the acceleration of new material discovery and cycle life prediction. However, the "black box" nature of many models limits the widespread application of machine learning, which in turn restricts its interpretability and scientific credibility. We propose a structured framework for machine learning in single-layer electrolyte research, consisting of five components: (i) solid electrolyte design, (ii) material characterization, (iii) electrode/electrolyte interface optimization, (iv) battery life prediction, and (v) dendrite inhibition. For each component, we identify its specific requirements and recommend appropriate methods to develop interpretable machine learning. Finally, we summarize the current challenges and propose corresponding suggestions and open-source toolchains, aiming to transition from "black box" predictions to mechanism-driven design, and accelerate the development of high-performance single-sided cell balancers (SSBs) for energy storage. This research was published under the title "Interpretable Machine Learning for Solid-State Batteries" in ACS Nano.
References: DOI: 10.1021/acsnano.5c21738
