Li Haoming Howard, Chen Qian, Ceder Gerbrand, Persson Kristin A
Department of Material Science and Engineering, University of California, Berkeley, California 94720, United States.
Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley 94720, United States.
ACS Appl Mater Interfaces. 2024 Dec 18;16(50):69379-69387. doi: 10.1021/acsami.4c15742. Epub 2024 Dec 9.
Advances in lithium-metal anodes have inspired interest in discovery of Li-free cathodes, most of which are natively found in their charged state. This is in contrast to today's commercial lithium-ion battery cathodes, which are more stable in their discharged state. In this study, we combine calculated cathode voltage information from both categories of cathode materials, covering 5577 and 2423 total unique structure pairs, respectively. The resulting voltage distributions with respect to the redox pairs and anion types for both classes of compounds emphasize design principles for high-voltage cathodes, which favor later Period 4 transition metals in their higher oxidation states and more electronegative anions like fluorine or polyanion groups. Generally, cathodes that are found in their charged, delithiated state are shown to exhibit voltages lower than those that are most stable in their lithiated state, in agreement with thermodynamic expectations. Deviations from this trend are found to originate from different anion distributions between redox pairs. In addition, a machine learning model for voltage prediction based on chemical formulas is trained and shows state-of-the-art performance when compared to two established composition-based ML models for material properties predictions, Roost and CrabNet.
锂金属负极的进展激发了人们对无锂正极的探索兴趣，其中大多数在其充电状态下天然存在。这与当今的商用锂离子电池正极形成对比，后者在放电状态下更稳定。在本研究中，我们结合了两类正极材料的计算阴极电压信息，分别涵盖了总共5577个和2423个独特的结构对。由此得到的两类化合物相对于氧化还原对和阴离子类型的电压分布强调了高压正极的设计原则，即更倾向于处于较高氧化态的第四周期后期过渡金属以及像氟或聚阴离子基团这样电负性更强的阴离子。一般来说，处于充电、脱锂状态的正极显示出的电压低于那些在锂化状态下最稳定的正极，这与热力学预期一致。发现偏离这一趋势源于氧化还原对之间不同的阴离子分布。此外，基于化学式的电压预测机器学习模型经过训练，与用于材料性能预测的两个已建立的基于成分的机器学习模型Roost和CrabNet相比，显示出了领先的性能。
