Multitarget Generate Electrolyte Additive for Lithium Metal Batteries.

ABSTRACT: Electrolyte additives are crucial for accelerating the commercialization of lithium metal batteries (LMBs), yet designing effective additives is challenging due to the need to balance conflicting properties, such as eectrochemical performance and nonflammability. To address this challenge, a deep learning-assisted generative model is developed for multiobjective optimization of electrolyte additives. Overcoming data scarcity, the dataset is expanded using a molecular categorization derivation method, increasing single-property data points to 70 095 multiproperty data points. Coupled with an asynchronous limited decoder and adversarial regulation strategy for latent distribution, this approach achieved 100% generative efficiency for structurally complex and diverse molecules in vast chemica

SUBMITTER: Liu X 

PROVIDER: S-EPMC12392859 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature