Evidence map›Paper›PMID 40735462›Full record

ArticleResearch (Washington, D.C.)2025

Machine Learning for Selecting High-Energy Phosphate Cathode Materials.

Yongchun Dang, Zechen Li, Yongchao Yu, Xiwei Bai, Li Wang, Xuelei Wang, Peng Liu, Chen Sun, Xunli Zhou, Zhenpo Wang and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Research (Washington, D.C.), 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. 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.

Yongchun DangNational Engineering Research Center of Electric Vehicles, Beijing Co-innovation Centre for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China.
Zechen LiBeijing Key Laboratory of Construction Tailorable Advanced Functional Materials and Green Applications, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
Yongchao YuNational Engineering Research Center of Electric Vehicles, Beijing Co-innovation Centre for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China.
Xiwei BaiInstitute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Li WangInstitute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China.
Xuelei WangInstitute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Peng LiuNational Engineering Research Center of Electric Vehicles, Beijing Co-innovation Centre for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China.
Chen SunBeijing Key Laboratory of Construction Tailorable Advanced Functional Materials and Green Applications, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
Xunli ZhouBeijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China.
Zhenpo WangNational Engineering Research Center of Electric Vehicles, Beijing Co-innovation Centre for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China.
Yongjie ZhaoBeijing Key Laboratory of Construction Tailorable Advanced Functional Materials and Green Applications, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
Xiangming HeInstitute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0001-7146-4097
Lei LiNational Engineering Research Center of Electric Vehicles, Beijing Co-innovation Centre for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China.ORCID https://orcid.org/0000-0003-1959-1683

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The limited energy density inherent in cathode materials remains a marked barrier to the widespread adoption of sodium-ion batteries. Despite considerable research efforts, the precise influence of atomic and crystalline configurations on energy density is not yet fully understood, creating a knowledge gap that hinders the rational design of advanced cathode materials. In this study, we propose a machine learning approach to systematically identify promising cathode materials with high energy densities. Our model highlights the critical roles of entropy and equivalent electronegativity, among other properties such as molecular mass, electron affinity, and average ionic radius. Based on these insights, we successfully synthesized Na

Identifiers

PMID40735462
PMCPMC12304885

What OpenQuestion holds

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Registered trials

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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.