ArticleProceedings of the National Academy of Sciences of the United States of America2016
Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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.
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Who cites it
17 citing papers in PubMed.
- Interpretable self-driving sputtering epitaxy reveals human-usable growth rules for β-GaNature communications · 2026Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Machine Learning in Polymer Research.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Advanced Computational Methods for Modeling, Prediction and Optimization-A Review.Materials (Basel, Switzerland) · 2024Review
- An update on the secretory functions of brown, white, and beige adipose tissue: Towards therapeutic applications.Reviews in endocrine & metabolic disorders · 2024Review
- Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery.Patterns (New York, N.Y.) · 2023Review
- Piezoelectric Materials and Sensors for Structural Health Monitoring: Fundamental Aspects, Current Status, and Future Perspectives.Sensors (Basel, Switzerland) · 2023Review
- Article
- Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics.Science advances · 2022Article
- Article
- Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts.Nature communications · 2020Article
- Accelerated Search for BaTiOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2019Article
- Data-Driven Materials Science: Status, Challenges, and Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2019Review
- Ultranarrow-Band Wavelength-Selective Thermal Emission with Aperiodic Multilayered Metamaterials Designed by Bayesian Optimization.ACS central science · 2019Article
- Multifunctional structural design of graphene thermoelectrics by Bayesian optimization.Science advances · 2018Article
- Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning.Nature communications · 2018Article
- Multi-objective Optimization for Materials Discovery via Adaptive Design.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An outstanding challenge in the nascent field of materials informatics is to incorporate materials knowledge in a robust Bayesian approach to guide the discovery of new materials. Utilizing inputs from known phase diagrams, features or material descriptors that are known to affect the ferroelectric response, and Landau-Devonshire theory, we demonstrate our approach for BaTiO
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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.