Fluoroethylene carbonate (FEC), as a key electrolyte component, has been extensively employed across diverse electrolyte systems owing to its excellent compatibility with different anode materials. However, its mechanistic role on the cathode side remains under debate due to the strong electron-withdrawing nature of the fluorine incorporation. Here, we demonstrate that lithium difluoro(oxalate)borate (LiDFOB) can effectively trigger the latent cathode-side functionality of FEC through a rationally designed dual-additive electrolyte. At the LiNi0.9Co0.05Mn0.05O2 cathode, oxidative cleavage of LiDFOB generates BOF2 intermediates that activate FEC and direct its decomposition toward LiF and B-O/B-F-rich inorganic species, constructing a compact and resilient cathode-electrolyte interphase (CEI). Simultaneously, the coupled reduction of FEC and DFOB- at the lithium metal anode yields a boron-rich, LiF-enriched solid electrolyte interphase (SEI) that enhances interfacial compatibility and suppresses dendrite growth. These LiDFOB-enabled, FEC-mediated interfacial pathways significantly improve ion transport and durability, delivering 73.4% capacity retention of Li||NCM9055 full cells after 1000 cycles at 4.7 V, versus 55.1% for the base electrolyte.
Molecular representations largely determine the learnability of quantum-chemical properties with machine learning. In order to find the most appropriate way to represent molecules in chemoinformatic studies, a comparative study of nine two-dimensional molecular fingerprints and three RDKit descriptor sets (PHYS, CONF, and PHCO) was conducted in terms of the prediction of five molecular properties by trained predictive machine learning models. The use of RDKit descriptor sets consistently yields more accurate results than hashed fingerprints across properties. Among fingerprints, Layered Fingerprint outperforms for global energy targets (Etot, Eee, Exc), whereas ECFP6 demonstrates better performance for atom-localized (Eatom) and thermodynamic targets (Cp). We further evaluate how the choice of hash function used during fingerprint construction affects representation quality and identify that noncryptographic hashing preserves locality and leads to better and more consistent outcomes than cryptographic hashing (SHA-256). This work provides mechanistic insights into how different molecular representations encode structural and physicochemical information, highlighting the merits and limits of descriptors for learning quantum-chemical properties. This offers practical guidance for selecting molecular representations and hashing strategies in designing and establishing pipelines for the artificial intelligence study of chemistry.
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