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.