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Sandeep Jain

Assistant Professor

Yeungnam University

South Korea

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About

Dr. Sandeep Jain is an Assistant Professor at Yeungnam University in South Korea. His research focuses on the application of machine learning techniques to predict the mechanical and magnetic properties of high entropy alloys and magnesium-based alloys. He has published several articles addressing the reduction of experimental dependency in materials science through advanced computational methods.

Articles (16)

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2026

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2024

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2024

Collaborators (5)

Phanikumar Gandham

Professor

Indian Institute of Technology Madras

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Ayan Bhowmik

Assistant Professor

Indian Institute of Technology, Delhi

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Yongho Jeon

Professor

Ajou University

SOUTH KOREA

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Rahul M R

Assistant professor

Indian Institute of Space Science and Technology

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Nokeun Park

Professor

Yeungnam University

SOUTH KOREA

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Sandeep Jain

Research Interests

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About

Articles (16)

Predicting the magnetic behaviour of CoFeNi high entropy alloys: Reducing experimental dependency through machine learning approaches

Microstructural Evolution and Densification Behavior of Ball-Milled AlFeCoNi Medium-Entropy Alloy Consolidated by Conventional Sintering

Accelerating Bulk Modulus Design of High-Entropy Alloys Through Explainable Machine Learning and SHAP-Driven Insights

Explainable Ensemble Machine Learning for Predicting Deposition Characteristics in Advanced Additive Manufacturing

Interpretable Machine-Learning Prediction of Atmospheric Zinc Corrosion Depth Under Diverse Environmental Conditions

Machine learning approaches for predicting and validating mechanical properties of Mg rare earth alloys for light weight applications

Predicting the magnetic behaviour of homogenized CoCrFeNiAlx high entropy alloys at different aluminium content and temperatures: Reducing experimental dependency through machine learning approaches

Prediction of alloying element effects on the mechanical behavior of high-pressure die-cast Mg-based alloys

Machine Learning Approaches for Predicting Mechanical Performance and Reducing Experimentation in Refractory High‐Entropy Alloys

Machine-learning-driven prediction of flow curves and development of processing maps for hot-deformed Ni–Cu–Co–Ti–Ta alloy

Reducing experimental dependency: Machine-learning-based prediction of Co effects on the mechanical properties of AlCrFeNiCox high-entropy alloys

Towards intelligent processing: Machine learning-based prediction of flow curves and hot workability maps for Ti71Fe25.15Sn3.85 alloy

Nanomanufacturing in the Biomedical Field

Investigation of Mechanical Properties, Fretting Wear, and Corrosion Behaviour of AA6063/Si3N4 Nanocomposites Fabricated via Friction Stir Processing

Effect of Ta on the Evolution of Phases and Mechanical Properties of Novel Seven Components Fe–Co–Ni–Cr–V–Al–Ta Eutectic High Entropy Alloys: Experimental Study and Numerical Simulation

Optimization of flow behavior models by genetic algorithm: A case study of aluminum alloy

Collaborators (5)

Phanikumar Gandham

Ayan Bhowmik

Yongho Jeon

Rahul M R

Nokeun Park

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