Evidence map›Paper›PMID 40314621›Full record

ArticleNano letters2025

Machine-Learning-Guided Screening of Advantageous Solvents for Solid Polymer Electrolytes in Lithium Metal Batteries.

Jiadong Shen, Junjie Chen, Xiaosa Xu, Jin Li, Zhenyu Wang, Pengzhu Lin, Zixiao Guo, Yu Wang, Jing Sun, Baoling Huang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nano letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Uniform and High LiSmall (Weinheim an der Bergstrasse, Germany) · 2025
    Review
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

11 authors.

Jiadong ShenDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.
Junjie ChenDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.
Xiaosa XuDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.
Jin LiDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.
Zhenyu WangDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.
Pengzhu LinDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.
Zixiao GuoDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.
Yu WangDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.ORCID 0000-0002-2972-3486
Jing SunDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.
Baoling HuangDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.ORCID 0000-0001-7507-5371
Tianshou ZhaoDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.ORCID 0000-0003-4825-2381

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Trace residual solvents in solid polymer electrolytes (SPEs) significantly affect electrolyte and interface properties, where optimal selection enhances the ionic conductivity and transference numbers. However, the solvent complexity hinders general screening methods. We establish a universal criterion linking electronic (highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO)) and macroscopic properties (dielectric constant, dipole moment, polarizability) via machine learning on an ∼10 000-solvent dataset from high-throughput density functional theory (DFT). Two solvents,

Indexed as

high-throughput DFT calculationslithium metal batteriesmachine learning algorithmsresidual solventssolid polymer electrolytes

Identifiers

PMID40314621
PMCPMC12082693

What OpenQuestion holds

Textmetadata
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.