Evidence map›Paper›PMID 42259809›Full record

ArticleNature communications2026

Navigating polymorph generation and distilled-potential development via entropy-symmetry landscapes for metal plasticity mechanisms.

Zeyuan Li, Taiqiao Liu, Xuhao Wan, Songpeng Zhao, Zhaofu Zhang, E Zhou, Wei Yu, Yijing Zuo, John Robertson, Sheng Liu and 1 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Zeyuan Li *School of Power and Mechanical Engineering, Wuhan University, Wuhan, China.ORCID http://orcid.org/0009-0005-9043-2345
Taiqiao Liu *School of Integrated Circuits, Wuhan University, Wuhan, China.
Xuhao WanDepartment of Physics and Astronomy, Rutgers University, Piscataway, USA.
Songpeng ZhaoSchool of Integrated Circuits, Wuhan University, Wuhan, China.
Zhaofu ZhangSchool of Integrated Circuits, Wuhan University, Wuhan, China.ORCID http://orcid.org/0000-0002-1406-1256
E ZhouSchool of Integrated Circuits, Wuhan University, Wuhan, China.
Wei YuSchool of Power and Mechanical Engineering, Wuhan University, Wuhan, China.
Yijing ZuoSchool of Physics and Technology, Wuhan University, Wuhan, China.
John RobertsonDepartment of Engineering, University of Cambridge, Cambridge, UK.
Sheng LiuSchool of Power and Mechanical Engineering, Wuhan University, Wuhan, China. shengliu@whu.edu.cn.ORCID http://orcid.org/0000-0001-6033-078X
Yuzheng GuoSchool of Power and Mechanical Engineering, Wuhan University, Wuhan, China. yguo@whu.edu.cn.ORCID http://orcid.org/0000-0001-9224-3816

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62361166628National Natural Science Foundation of China (National Science Foundation of China) T2525025National Natural Science Foundation of China (National Science Foundation of China) U2241244
6 · The paper itself

Abstract

Artificial intelligence has advanced crystal design, yet unifying crystal structure prediction with thermodynamics-driven structure-property modelling remains challenging owing to divergent methodological foundations. Here we show that an integrated framework called PolymorphGen‑MLPKD, driven by physically meaningful entropy-symmetry landscapes, enables targeted generation of polymorphs and concurrent structure-property modelling. The framework captures phase behaviour across diverse crystal systems, generates paracrystalline structures, reveals graphite-diamond transition pathways, and produces knowledge‑distilled potentials that transfer cross‑scale accuracy with a 10

Identifiers

PMID42259809
PMCPMC13246779

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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.