An Integrated NLP-ML Framework for Property Prediction and Design of Steels.

Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis

Abstract read

In one paragraph

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. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Kiran DevrajuFaculty of Science, Engineering and Built Environment, Deakin University, Waurn Ponds, VIC, Australia.

Adithya Umanath RaiFaculty of Science, Engineering and Built Environment, Deakin University, Waurn Ponds, VIC, Australia.

Jose ThomsonFaculty of Science, Engineering and Built Environment, Deakin University, Waurn Ponds, VIC, Australia.

Marzie GhorbaniFaculty of Science, Engineering and Built Environment, Deakin University, Waurn Ponds, VIC, Australia.

Shujing ZhaoCollege of Systems and Society, The Australian National University, Acton, ACT, Australia.

Nick BirbilisFaculty of Science, Engineering and Built Environment, Deakin University, Waurn Ponds, VIC, Australia.ORCID https://orcid.org/0000-0002-7910-7470

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper introduces a data-driven framework designed to accelerate the property prediction, and hence the discovery and development of steels. The study leverages natural language processing and machine learning techniques to analyse a dataset containing steel compositions, (thermo)mechanical processing, and mechanical properties. By integrating unsupervized machine learning for process classification (via natural language processing-assisted clustering) and supervised regression models for property prediction, the framework enables an efficient exploration method to study steels. A predictive accuracy of R

Indexed as

artificial intelligencemachine learningmaterials informaticssteelsustainable design

Identifiers

PMID41944362

DOI10.1002/advs.202521457

PMCPMC13325829

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LicenceCC BY

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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.