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Electrolyte anions are pivotal for lithium battery performance, yet their fundamental electronic structural properties are not well understood. In this work, we employ a combination of negative-ion photoelectron spectroscopy (NIPES), ab initio calculations, and molecular dynamics (MD) simulations to investigate the electronic structures of three representative electrolyte anions. This multiscale approach enables us to elucidate how their intrinsic electronic properties govern anion–solvent interactions in gas-phase clusters, as well as lithium-ion (Li⁺) solvation structures and ion transport behavior in the condensed phase. NIPES reveals that difluoro(oxalato)borate (DFOB^–), bis(fluorosulfonyl)imide (FSI^–), and bis(oxalato)borate (BOB^–) all exhibit high electron binding energies, with vertical/adiabatic detachmentmore » energies increasing from DFOB^– (6.09/5.70 eV) to FSI^– (6.80/6.10 eV) to BOB^– (6.82/6.40 eV), correlating with enhanced oxidation stability. Ab initio calculations reveal that DFOB^–/FSI^––solvent complexes bind Li⁺ ∼ 10 kcal/mol stronger than BOB^– series, aligning with the strength of a Li⁺–anion model. DFOB^– exhibits pronounced charge localization on both oxygen and fluorine atoms, enabling their involvement in Li⁺ coordination. In contrast, fluorine atoms in FSI^– are largely electron-depleted and remain excluded from direct Li⁺ binding. MD simulations further demonstrate that LiDFOB and LiFSI systems exhibit Li⁺ diffusion coefficients three and five times higher than those of LiBOB across four common solvents. Notably, LiFSI salt in acetonitrile (AN) exhibits the fastest Li⁺ diffusion among 12 electrolyte systems, highlighting the synergistic effect of FSI^– and AN in promoting ion mobility. In conclusion, these findings provide a molecular-level understanding of the critical roles of anion and its microsolvation in optimizing Li⁺ diffusion dynamics, once again emphasizing the positioning of FSI^– and DFOB^– as prime candidates for next-generation electrolytes.« less

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphicalmore » user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.« less