Evidence map›Paper›PMID 40534252›Full record

ArticleAdvanced materials (Deerfield Beach, Fla.)2025

Multitarget Generate Electrolyte Additive for Lithium Metal Batteries.

Xiangyang Liu, Jianchun Chu, Sa Xue, Daquan Wang, Zhuoyang Lu, Meng Zhang, Yongqi Liu, Xin Xu, Yilin Zhang, Jiangang Long and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Multitarget Generate Electrolyte Additive for Lithium Metal Batteries.Advanced materials (Deerfield Beach, Fla.) · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Xiangyang LiuKey Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Jianchun ChuKey Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Sa XueKey Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Daquan WangSchool of Chemistry, Xi'an Jiaotong University, Xi'an, 710049, China.
Zhuoyang LuKey Laboratory of Biomedical Information Engineering of MOE, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
Meng ZhangApplied Science and Technology Graduate Group, University of California, Berkeley, CA, 94720, USA.
Yongqi LiuKey Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Xin XuSchool of Chemistry, Xi'an Jiaotong University, Xi'an, 710049, China.
Yilin ZhangKey Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Jiangang LongKey Laboratory of Biomedical Information Engineering of MOE, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
Lingjie MengSchool of Chemistry, Xi'an Jiaotong University, Xi'an, 710049, China.
Jiayin YuanDepartment of Chemistry, Stockholm University, Svante Arrheniusväg 16C, Stockholm, 106 91, Sweden.ORCID https://orcid.org/0000-0003-1016-5135
Maogang HeKey Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.

Funding

China Scholarship CouncilFundamental Research Funds for the Central Universities xtr052024005Innovation Capability Support Program of Shaanxi 2024CX-GXPT-12Marie Skłodowska-Curie Actions Doctoral Network - PASSION 101147049National Natural Science Foundation of China 22205173National Natural Science Foundation of China 52376165National Natural Science Foundation of China 52422607Wenner-Gren-Foundation in Sweden UPD2023-0025
6 · The paper itself

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 chemical space. The method is validated by discovering 2,4-bis(2-fluoroethoxy) tetrafluorocyclotriphosphazene (DFEPN), a novel additive with excellent flame resistance and stable dual electrode/electrolyte interphases. In a Li||LiFePO

Indexed as

artificial intelligenceelectrolytegenerative modellithium batterymolecular design

Identifiers

PMID40534252
PMCPMC12392859

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.