An advanced machine learning framework for predicting and experimentally validating Nb and Ta effects on the mechanical behavior of high-entropy alloys with reduced experimental dependency.

Sandeep Jain, Ayan Bhowmik, Nokeun Park, Pradyumn Kumar Arya, Ankur Srivastava

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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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.

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1 citing paper in PubMed.

Explainable Ensemble Machine Learning for Predicting Deposition Characteristics in Advanced Additive Manufacturing.Micromachines · 2026
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5 · Who and what money

Authors and funding

5 authors.

Sandeep JainSchool of Materials Science and Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea. sandeepmbm20@gmail.com.

Ayan BhowmikDepartment of Materials Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.

Nokeun ParkSchool of Materials Science and Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea. nokeun_park@yu.ac.kr.

Pradyumn Kumar AryaDepartment of Mechanical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.

Ankur SrivastavaDepartment of Mechanical Engineering, Manipal University Jaipur, Jaipur, 303007, India. ankur.srivastava@jaipur.manipal.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study employs machine learning (ML) models such as Decision Tree (DT), Extra Tree (ET), K-Nearest Neighbors (KNN), Support Vector Regressor (SVR) and Lasso to predict how Ta and Nb content effects the mechanical behavior of the CoCrFeNi HEAs at diverse compositions. ET and KNN models developed as the most effective forecasters, accomplishing high R

Indexed as

High entropy alloysMachine learningMechanical behaviourRefractory materials

Identifiers

PMID42091614

DOI10.1038/s41598-026-45211-y

PMCPMC13338202

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