Molecular Dynamics Simulation and Artificial Intelligence-Driven Development of New Lithium Electrolytes with High Ionic Conductivity and Understanding Ion Transport

High ionic conductivity electrolytes are vital for ensuring robust lithium-ion battery performance, especially in low-temperature environments. In this study, we systematically investigated a novel chemical space comprising 2604 electrolyte formulations using high-throughput molecular dynamics (MD) simulations, integrating the OPLS-AA force field with the RESP2 charge model. This methodology accurately replicated Li⁺ solvation shell structures and identified numerous innovative electrolytes exhibiting high room-temperature ionic conductivities more than 10 mS/cm, and many of which were experimentally validated for the first time. By leveraging MD data sets and the machine learning method, the composition-property relationships governing Li⁺ solvation shell structure and ion transport in electrolytes were elucidated. Li⁺ solvation shell structures are primarily influenced by solvent concentration, molecular topology, and surface charge distribution, with the higher solvent concentrations enhancing Li⁺-molecule coordination numbers. Ionic conductivity of electrolyte is predominantly determined by viscosity, and the low-viscosity components such as PF₆^-, DOL, DME, EA, and DMC boost ionic conductivity, while TFSI^-, DEC, and EMC tend to reduce it. Additionally, the high coordination numbers with weakly coordinating solvents leading to the larger localized Li⁺ interactions further enhance ion transport in electrolyte. Molecular descriptors, including HallKierAlpha and MaxPartialCharge, exhibit strong correlations with ionic conductivity, serving as the effective metrics for the large-scale screening tasks. Consequently, the optimal high-conductivity electrolytes should incorporate low-viscosity solvents with high coordination numbers, strong Li⁺ binding energies, elevated HallKierAlpha values, and reduced MaxPartialCharges. This synergistic integration of high-throughput simulations and machine learning offers a powerful approach for the discovery of advanced electrolytes.