Evidence map›Paper›PMID 40671603›Full record

ArticleScience progress

Prediction of piezoelectric properties of NBT-based ceramics based on deep neural networks.

Xin Zhang, Yalong Liang, Jiachen Wang, Pan Chen, Pei Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science progress. 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. 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

5 authors.

Xin ZhangHenan International Joint Laboratory of Structural Mechanics and Computational Simulation, College of Architectural and Civil Engineering, Huanghuai University, Zhumadian, China.
Yalong LiangSchool of Architecture and Civil Engineering, Xinyang Normal University, Xinyang, China.
Jiachen WangHenan International Joint Laboratory of Structural Mechanics and Computational Simulation, College of Architectural and Civil Engineering, Huanghuai University, Zhumadian, China.ORCID 0009-0005-2785-9311
Pan ChenHenan International Joint Laboratory of Structural Mechanics and Computational Simulation, College of Architectural and Civil Engineering, Huanghuai University, Zhumadian, China.
Pei LiHenan International Joint Laboratory of Structural Mechanics and Computational Simulation, College of Architectural and Civil Engineering, Huanghuai University, Zhumadian, China.ORCID 0000-0001-6275-1342

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, the rapid advancement of materials science has underscored the significance and complexity of understanding and optimizing the properties of piezoelectric materials. Traditional analytical methods have encountered difficulties in addressing the challenges posed by variable material compositions and intricate processing conditions. However, deep neural networks (DNNs) present new opportunities in this field due to their robust capabilities in nonlinear data processing and pattern recognition. This study employs DNN to model the relationship between the composition and processing conditions of piezoelectric materials and their electrical properties. Through quenching experiments, it was observed that the electrical properties of Na

Indexed as

deep neural networkslocal polar heterogeneityMachine learningpiezoelectric ceramicspiezoelectric propertypredictive methodology

Identifiers

PMID40671603
PMCPMC12276500

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.