机器学习引导筛选锂金属电池中固体聚合物电解质的优势溶剂
Machine Learning-Guided Screening of Advantageous Solvents for Solid Polymer Electrolytes in Lithium Metal Batteries
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中文总结 AI 辅助
本研究通过机器学习建立关联溶剂电子与宏观性质的筛选准则，识别出优势溶剂，将其掺入SPE后制备的锂金属电池性能优异，优于多种常用溶剂，为SPE设计提供了新范式。
中文摘要 AI 辅助
固体聚合物电解质（SPE）中的痕量残留溶剂会显著影响电解质及界面性能，最优溶剂选择可提升离子电导率与迁移数，但溶剂的复杂性阻碍了通用筛选方法的开发。本研究基于高通量DFT得到的约10000种溶剂数据集，通过机器学习建立了关联电子性质（最高占据分子轨道HOMO、最低未占分子轨道LUMO）与宏观性质（介电常数、偶极矩、极化率）的通用准则，识别出两种优势溶剂：N-甲氧基-N-甲基-2,2,2-三氟乙酰胺和2,2,2-三氟-N,N-二甲基乙酰胺。将痕量N-甲氧基-N-甲基-2,2,2-三氟乙酰胺掺入聚偏二氟乙烯-共-六氟丙烯（poly(vinylidene fluoride-co-hexafluoropropylene)）基体后，所得电解质的电化学窗口达4.5 V，30℃下离子电导率为5.5×10^-4 S cm^-1，Li+迁移数为0.78；组装的电池在LiFePO4体系中循环500次后容量保持率为86.7%，在LiNi0.9Co0.05Mn0.05O2体系中2C倍率下循环200次后容量保持率达98.7%，性能优于2,2,2-三氟-N,N-二甲基乙酰胺、二甲基甲酰胺、N-甲基-2-吡咯烷酮及二甲基亚砜。该协同作用实现了离子传输、稳定性与循环耐久性的平衡，推动了更安全、高能量锂金属电池的发展；本研究的整合方法建立了用于合理SPE设计的溶剂筛选范式，加速了下一代电池的开发。
英文摘要
Trace residual solvents in solid polymer electrolytes (SPEs) significantly affect electrolyte and interface properties, where optimal selection enhances ionic conductivity and transference numbers. However, solvent complexity hinders general screening methods. We establish a universal criterion linking electronic (HOMO, LUMO) and macroscopic properties (dielectric constant, dipole moment, polarizability) via machine learning on an approximately 10,000-solvent dataset from high-throughput DFT. Two solvents, N-methoxy-N-methyl-2,2,2-trifluoroacetamide and 2,2,2-trifluoro-N,N-dimethylacetamide, were identified. Experimental incorporation of trace N-methoxy-N-methyl-2,2,2-trifluoroacetamide into a poly(vinylidene fluoride-co-hexafluoropropylene) matrix achieves a 4.5 V window, 5.5x10^-4 S cm^-1 conductivity (30 C), and 0.78 Li+ transference number. The cell retains 86.7% capacity over 500 cycles (LiFePO4) and 98.7% after 200 cycles at 2C (LiNi0.9Co0.05Mn0.05O2), outperforming 2,2,2-trifluoro-N,N-dimethylacetamide, dimethylformamide, N-methyl-2-pyrrolidone, and dimethyl sulfoxide. This synergy enables balanced ion transport, wide stability, and cycling durability, advancing safer, high-energy lithium metal batteries. Our integrated approach establishes a solvent screening paradigm for rational SPE design, accelerating next-generation battery development.
