ArticleNature communications2024
Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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.
The trial behind it
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Who cites it
13 citing papers in PubMed.
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Robot-Assisted Study of a Near-Infrared Dye in Perovskite Solar Cells.ACS applied materials & interfaces · 2026Article
- RobInHood: a robotic chemist in a fume hood.Digital discovery · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- What are the limits to biomedical research acceleration through general-purpose AI?Scientific reports · 2026Review
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- XQueryer: an intelligent crystal structure identifier for powder X-ray diffraction.National science review · 2025Article
- Accelerated Development of Vapor Deposition Technology for Efficient Perovskite Solar Cells via Accurate and Practical Machine Learning Tools.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.Nanophotonics (Berlin, Germany) · 2025Review
- Machine Learning-Driven Design of Fluorescent Materials: Principles, Methodologies, and Future Directions.Nanomaterials (Basel, Switzerland) · 2025Review
- Affordable Automated Modules for Lab-Scale High-Throughput Synthesis of Inorganic Materials.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- A self-supervised robotic system for autonomous contact-based spatial mapping of semiconductor properties.Science advances · 2025Article
- Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization.Nature communications · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Accelerating perovskite solid solution discovery and sustainable synthesis is crucial for addressing challenges in wireless communication and biosensors. However, the vast array of chemical compositions and their dependence on factors such as crystal structure, and sintering temperature require time-consuming manual processes. To overcome these constraints, we introduce an automated materials discovery approach encompassing machine learning (ML) assisted material screening, robotic synthesis, and high-throughput characterization. Our proposed platform for rapid sintering and dielectric analysis streamlines the characterization of perovskites and the discovery of disordered materials. The setup has been successfully validated, demonstrating processing materials within minutes, in stark contrast to conventional procedures that can take hours or days. Following setup validation with established samples, we showcase synthesizing single-phase solid solutions within the barium family, such as (Ba
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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.