ArticleNature communications2023
Toward the design of ultrahigh-entropy alloys via mining six million texts.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 80 citations in OpenAlex.
- Template-Confined Synthesis of 1 nm High-Entropy-Alloy Nanoparticle Library for Electrocatalysis.ACS nano · 2026Article
- Exploring the relative influence of atomic parameters on solid solution strengthening.Nature communications · 2025Article
- Tracking 35 years of progress in metallic materials for extreme environments via text mining.Scientific reports · 2025Article
- A keyword-based approach to analyzing scientific research trends: ReRAM present and future.Scientific reports · 2025Article
- Enhancing corrosion-resistant alloy design through natural language processing and deep learning.Science advances · 2023Article
- Fatigue database of complex metallic alloys.Scientific data · 2023Article
- Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature.Science advances · 2023Article
- The Surface Properties of Implant Materials by Deposition of High-Entropy Alloys (HEAs).Nanomaterials (Basel, Switzerland) · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It has long been a norm that researchers extract knowledge from literature to design materials. However, the avalanche of publications makes the norm challenging to follow. Text mining (TM) is efficient in extracting information from corpora. Still, it cannot discover materials not present in the corpora, hindering its broader applications in exploring novel materials, such as high-entropy alloys (HEAs). Here we introduce a concept of "context similarity" for selecting chemical elements for HEAs, based on TM models that analyze the abstracts of 6.4 million papers. The method captures the similarity of chemical elements in the context used by scientists. It overcomes the limitations of TM and identifies the Cantor and Senkov HEAs. We demonstrate its screening capability for six- and seven-component lightweight HEAs by finding nearly 500 promising alloys out of 2.6 million candidates. The method thus brings an approach to the development of ultrahigh-entropy alloys and multicomponent materials.
Indexed as
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What OpenQuestion holds
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.