Evidence map›Paper›PMID 42195580›Full record

ArticleMaterials (Basel, Switzerland)2026

Machine Learning-Based Prediction of Stacking Fault Energy in High-Manganese Steels: A Comparative Study of Ensemble and Kernel Methods.

Saurabh Tiwari, Seong Jun Heo, Nokeun Park

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Saurabh TiwariSchool of Materials Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.ORCID 0000-0001-5029-9774
Seong Jun HeoSchool of Materials Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Nokeun ParkSchool of Materials Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.ORCID 0000-0002-7863-1457

Funding

Ministry of Education (MOE) and the Gyeongsangbuk-do Republic of Korea. 2025-RISE-15-115
6 · The paper itself

Abstract

Accurate prediction of the stacking fault energy (SFE) is critical for controlling deformation mechanisms, specifically transformation-induced plasticity (TRIP) and twinning-induced plasticity (TWIP), in high-manganese (high-Mn) austenitic steels, which are of growing importance in automotive and structural applications that demand exceptional strength-ductility combinations. This study presents a systematic comparative evaluation of six supervised machine learning (ML) models-Multiple Linear Regression (MLR), Random Forest (RF), Extra Trees (ETs), Gradient Boosting (GB), Support Vector Regression (SVR), and a stacking ensemble-trained on a curated, outlier-cleaned experimental database of Fe-Mn-C-Si-Al-Cr-Ni-N spanning SFE values from 5.0 to 63.0 mJ/m

Indexed as

alloy designextra treesgradient boostinghigh-manganese steelmachine learningmaterials informaticsrandom foreststacking ensemblestacking fault energyTRIPTWIP

Identifiers

PMID42195580
PMCPMC13208557

What OpenQuestion holds

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Registered trials

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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.