Interpretable Machine Learning with Prediction Uncertainty Quantification for

Xiaohui Yuan, Yalong Liang, Bang Lu, Gaochao Zhao, Pei Li

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 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

5 authors.

Xiaohui YuanCollege of Architecture and Civil Engineering, Xinyang Normal University, Xinyang 464000, China.

Yalong LiangCollege of Architecture and Civil Engineering, Xinyang Normal University, Xinyang 464000, China.

Bang LuHenan International Joint Laboratory of Structural Mechanics and Computational Simulation, College of Architectural and Civil Engineering, Huanghuai University, Zhumadian 463000, China.

Gaochao ZhaoHenan International Joint Laboratory of Structural Mechanics and Computational Simulation, College of Architectural and Civil Engineering, Huanghuai University, Zhumadian 463000, China.

Pei LiCentre for Industrial Mechanics, Institute of Mechanical and Electrical Engineering, University of Southern Denmark, 6400 Sønderborg, Denmark.ORCID 0000-0001-6275-1342

Funding

2025 Henan Province International Science and Technology Cooperation Project 252102521011Postgraduate Education Reform and Quality Improvement Project of Henan Province YJS2023JD52Postgraduate Education Reform and Quality Improvement Project of Henan Province YJS2025GZZ48Research Merit-based Funding Program for Overseas Educated Personnel in Henan Province Letter of Henan Human Resources and Social Security Office [2025] No. 37Scientific Research Foundation of Graduate School of Xinyang Normal University N.A.Zhumadian 2023 Major Science and Technology Special Project ZMD SZDZX2023002Zhumadian 2024 Major Science and Technology Special Project ZMDSZDYF2024007

6 · The paper itself

Abstract

The accelerated discovery of high-performance lead-free piezoelectric ceramics is hindered by the vast compositional space and the limited interpretability of conventional machine learning (ML) models. Here, we propose a physics-informed and interpretable ML framework with integrated uncertainty quantification to predict and understand the piezoelectric coefficient

Indexed as

interpretable machine learningKNN-based piezoceramicsSHAP analysisSISSO descriptoruncertainty quantification

Identifiers

PMID41828217

DOI10.3390/ma19050948

PMCPMC12985709

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.