ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022
Using Feature-Assisted Machine Learning Algorithms to Boost Polarity in Lead-Free Multicomponent Niobate Alloys for High-Performance Ferroelectrics.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Alkaline Earth Niobates as New Photocatalysts: Interplay between Structure, Ferroelectric Nature, and Photocatalytic Activity.ACS materials Au · 2026Article
- Interpretable Machine Learning with Prediction Uncertainty Quantification forMaterials (Basel, Switzerland) · 2026Article
- XGBoost-Based Modeling of Electrocaloric Property: A Bayesian Optimization in BCZT Electroceramics.Materials (Basel, Switzerland) · 2025Article
- Oxygen-Doped 2D InAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials.Materials (Basel, Switzerland) · 2024Review
- Using Feature-Assisted Machine Learning Algorithms to Boost Polarity in Lead-Free Multicomponent Niobate Alloys for High-Performance Ferroelectrics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
To expand the unchartered materials space of lead-free ferroelectrics for smart digital technologies, tuning their compositional complexity via multicomponent alloying allows access to enhanced polar properties. The role of isovalent A-site in binary potassium niobate alloys, (K,A)NbO
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Registered trials
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.