ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Diffusion-Model-Driven Discovery of Ferroelectrics for Photocurrent Applications.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
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.
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Who cites it
2 citing papers in PubMed.
- Are diffusion models ready for materials discovery in unexplored chemical space?Patterns (New York, N.Y.) · 2026Article
- Diffusion-Model-Driven Discovery of Ferroelectrics for Photocurrent Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Ferroelectric materials are vital for next-generation memory and photovoltaic technologies, yet their discovery is limited to a few known prototypes. Here, we present a design framework that integrates diffusion-model-based crystal generation with multi-fidelity screening to the discovery of ferroelectrics. Using MatterGen, we generated 12 800 candidate structures and applied a pipeline combining diverse machine-learning tools and density functional theory calculations. This process revealed two promising candidates, Ca
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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.