ArticleNature communications2018
Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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The trial behind it
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Who cites it
38 citing papers in PubMed.
- Machine-learning-guided inverse design of lead-free relaxors enabled by multimodal literature mining.Nature communications · 2026Article
- High-entropy 1D halide perovskite piezoelectrics found by megalibrary synthesis and rapid nonlinear optical screening.Science advances · 2026Article
- Organic-Inorganic Metal Halide Perovskites: Toward Stability, Chirality, and AI-Guided Discovery.ACS central science · 2026Review
- Active learning for the accelerated discovery of complex concentrated NiCoCr alloys in additive manufacturing.NPJ advanced manufacturing · 2026Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Data-Mining-Aided-Material Design of Doped LaMnOMaterials (Basel, Switzerland) · 2025Article
- How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?National science review · 2025Review
- Machine Learning Methodologies Applied to Magnetocaloric Perovskites Discovery.Journal of chemical information and modeling · 2025Article
- Advanced Materials for Energy Harvesting and Soft Robotics: Emerging Frontiers to Enhance Piezoelectric Performance and Functionality.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies.Accounts of chemical research · 2024Article
- Machine-Guided Discovery of Acrylate Photopolymer Compositions.ACS applied materials & interfaces · 2024Article
- Toward Ecofriendly Piezoelectric Ceramics-Reduction of Energy and Environmental Footprint from Conceptualization to Deployment.Global challenges (Hoboken, NJ) · 2023Review
- Exploring the Optimal Alloy for Nitrogen Activation by Combining Bayesian Optimization with Density Functional Theory Calculations.ACS omega · 2022Article
- Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization.ACS energy letters · 2022Article
- Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors.Digital discovery · 2022Article
- Formula Graph Self-Attention Network for Representation-Domain Independent Materials Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Screening Perovskites from ABOACS omega · 2022Article
- Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics.Science advances · 2022Article
- A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery.Materials (Basel, Switzerland) · 2022Review
- Discovery of Lead-Free Perovskites for High-Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Experimental search for high-temperature ferroelectric perovskites is a challenging task due to the vast chemical space and lack of predictive guidelines. Here, we demonstrate a two-step machine learning approach to guide experiments in search of xBi[Formula: see text]O
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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.