基于材料基因组方法的锂电池新材料开发

近年来,在锂二次电池新材料的研发过程中逐渐建立了基于材料基因组思想的高通量计算理论工具与研究平台.在该平台上,通过将不同精度的计算方法组合,实现了基于离子输运性质的材料筛选;通过将信息学中数据挖掘算法引入高通量计算数据的分析,证实了材料大数据解读的可行性.上述平台实现了在锂电池固体电解质的高通量筛选、优化和设计上进行新材料研发的示范应用,通过高通量计算筛选获得了两种可用于富锂正极包覆材料的化合物Li_2SiO_3和Li2SnO_3,有效改善了富锂正极的循环稳定性;通过对掺杂策略的高通量筛选,获得了提高固体电解质β-Li_3PS_4离子电导率和稳定性的方案;通过高通量结构预测设计了全新的氧硫化物固体电解质LiAlSO;并在零应变电极材料结构与性能的构效关系研究中进行了大数据分析的尝试,分析了零应变电极材料的设计依据.上述材料基因组方法在锂电池材料研发中的应用为在其他类型材料研发中推广这种新的研发模式提供了可能.

Development of new lithium battery materials by material genome initiative

After the continuous research on the discovering new materials based on theoretical methods and material genome initiative, the high-throughput simulation platform is established. With this new research mode and platform, the screening, optimization and design of lithium battery materials are realized by using lithium migration properties as criteria. The attempt at introducing machine learning method into material design is also made. With the high-throughput bond-valence calculations, two coating materials for Li-rich cathode are found, the modified β -Li₃PS₄ and a new layered oxysulfide as novel lithium superionic conductors are designed, and the relationship between the volume change of electrode during delithiation and the atomic structure is investigated. The application of the material genome method to the development of lithium battery materials provides the possibility to promote this new research and development model in other types of materials.