Evidence map›Paper›PMID 41662506›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Machine-Learning-Guided Design of Incommensurate Antiferroelectrics via Field-Driven Phase Engineering.

Ke Xu, Xiaoming Shi, Zhaochen Xi, Shouzhe Dong, Changqing Guo, Rongzhen Gao, Letao Yang, Jing Wang, Di Zhou, Houbing Huang

Abstract read
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In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Ke XuSchool of Materials Science and Engineering & School of Interdisciplinary Science, Beijing Institute of Technology, Beijing, China.
Xiaoming ShiSchool of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China.
Zhaochen XiSchool of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an, China.
Shouzhe DongSchool of Materials Science and Engineering & School of Interdisciplinary Science, Beijing Institute of Technology, Beijing, China.
Changqing GuoSchool of Materials Science and Engineering & School of Interdisciplinary Science, Beijing Institute of Technology, Beijing, China.
Rongzhen GaoSchool of Materials Science and Engineering & School of Interdisciplinary Science, Beijing Institute of Technology, Beijing, China.
Letao YangSchool of Materials Science and Engineering & School of Interdisciplinary Science, Beijing Institute of Technology, Beijing, China.
Jing WangSchool of Materials Science and Engineering & School of Interdisciplinary Science, Beijing Institute of Technology, Beijing, China.
Di ZhouSchool of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an, China.
Houbing HuangSchool of Materials Science and Engineering & School of Interdisciplinary Science, Beijing Institute of Technology, Beijing, China.ORCID https://orcid.org/0000-0002-8006-3495

Funding

Beijing Natural Science Foundation 2242057National Natural Science Foundation of China 52302277National Natural Science Foundation of China 52372100National Natural Science Foundation of China 52472119National Natural Science Foundation of China 525B2020National Natural Science Foundation of China 92463306
6 · The paper itself

Abstract

Antiferroelectrics, defined by antiparallel polarization configurations, have emerged as promising dielectric materials for high-performance energy storage applications. The energy storage density and efficiency of antiferroelectrics are governed by the regulation of electric field-driven antiferroelectric-ferroelectric phase transitions (and their reversibility). Herein, we propose a machine learning-guided phase-field simulation framework to accelerate the design of energy storage ceramics. Taking PbZrO

Indexed as

antiferroelectricsenergy storage performancemachine learningmulti‐scale regulationphase‐field simulations

Identifiers

PMID41662506
PMCPMC13088312

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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.