ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
From Small Data Modeling to Large Language Model Screening: A Dual-Strategy Framework for Materials Intelligent Design.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High-Performance Cu Alloys.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advancing battery research through large language models: A review.Innovation (Cambridge (Mass.)) · 2026Review
- From Small Data Modeling to Large Language Model Screening: A Dual-Strategy Framework for Materials Intelligent Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Small data in materials present significant challenges to constructing highly accurate machine learning models, severely hindering the widespread implementation of data-driven materials intelligent design. In this study, the Dual-Strategy Materials Intelligent Design Framework (DSMID) is introduced, which integrates two innovative methods. The Adversarial domain Adaptive Embedding Generative network (AAEG) transfers data between related property datasets, even with only 90 data points, enhancing material composition characterization and improving property prediction. Additionally, to address the challenge of screening and evaluating numerous alloy designs, the Automated Material Screening and Evaluation Pipeline (AMSEP) is implemented. This pipeline utilizes large language models with extensive domain knowledge to efficiently identify promising experimental candidates through self-retrieval and self-summarization. Experimental findings demonstrate that this approach effectively identifies and prepares new eutectic High Entropy Alloy (EHEA), notably Al
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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.