Dong Jinjin, Yang Wenjun, Liu Haolin, Wu Jingwen, Wang Zongguo
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, School of Mechanical Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China.
ACS Appl Mater Interfaces. 2025 Jul 23;17(29):41868-41882. doi: 10.1021/acsami.5c03645. Epub 2025 Jul 8.
Lithium-ion (Li-ion) solid-state batteries (SSBs) are highly regarded for their exceptional energy density and prolonged operational lifespan. However, concerns regarding their sustainability have arisen due to the uneven global distribution of Li resources and Li's relatively low abundance in the Earth's crust. Consequently, significant interest has shifted toward developing alternative SSBs, such as sodium (Na), magnesium (Mg) and Aluminum (Al)-ion batteries. A key challenge in this pursuit is efficiently identifying viable solid-state electrolytes (SEs) from the vast chemical space, particularly for Na and Mg ions. This study introduces a generalized framework based on machine learning for effectively screening high-performance garnet-type SEs. Utilizing specifically designed chemical descriptors, ML models predict the thermal stability and electrical conductivity of garnet-type SEs, achieving predictive accuracies of 94% and 89%, respectively. The chemical factors influencing stability and conductivity are identified and validated through interpretability analysis. Leveraging these models, 1764 garnet-type SEs exhibiting high thermal stability and wide band gaps were screened from a database of 43,732 compounds. Furthermore, 44 garnet-type SEs with favorable environmental and economic advantages were selected, and verified through first-principles calculations using density functional theory. Given their cost-effectiveness and high performance, these SEs hold great potential for application in Na, Mg, and Al ion SSBs. This study provides crucial insights into developing SSB materials, advances sustainable energy storage, and offers key perspectives for exploring material systems within specific space groups.
锂离子（Li-ion）固态电池（SSB）因其卓越的能量密度和延长的使用寿命而备受关注。然而，由于锂资源在全球分布不均以及锂在地壳中的丰度相对较低，人们对其可持续性产生了担忧。因此，人们的兴趣已大幅转向开发替代固态电池，如钠（Na）、镁（Mg）和铝（Al）离子电池。在这一探索过程中的一个关键挑战是如何从广阔的化学空间中高效识别可行的固态电解质（SE），特别是对于钠离子和镁离子。本研究引入了一个基于机器学习的通用框架，用于有效筛选高性能石榴石型固态电解质。利用专门设计的化学描述符，机器学习模型预测了石榴石型固态电解质的热稳定性和电导率，预测准确率分别达到94%和89%。通过可解释性分析确定并验证了影响稳定性和电导率的化学因素。利用这些模型，从43732种化合物的数据库中筛选出1764种具有高热稳定性和宽带隙的石榴石型固态电解质。此外，还选择了44种具有良好环境和经济优势的石榴石型固态电解质，并通过使用密度泛函理论的第一性原理计算进行了验证。鉴于其成本效益和高性能，这些固态电解质在钠、镁和铝离子固态电池中具有巨大的应用潜力。本研究为开发固态电池材料提供了关键见解，推动了可持续储能发展，并为探索特定空间群内的材料体系提供了关键视角。
