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Russlan Jaafreh
Russlan Jaafreh
PhD Candidate, SKKU University
Verified email at skku.edu
Title
Cited by
Cited by
Year
Lattice thermal conductivity: an accelerated discovery guided by machine learning
R Jaafreh, YS Kang, K Hamad
ACS Applied Materials & Interfaces 13 (48), 57204-57213, 2021
362021
Machine learning guided discovery of super-hard high entropy ceramics
R Jaafreh, YS Kang, JG Kim, K Hamad
Materials Letters 306, 130899, 2022
222022
A deep learning perspective into the figure-of-merit of thermoelectric materials
R Jaafreh, KY Seong, JG Kim, K Hamad
Materials Letters 319, 132299, 2022
152022
Effect of CaO on structure and properties of AZ61 magnesium alloy
UM Chaudry, Y Noh, G Han, R Jaafreh, TS Jun, K Hamad
Materials Science and Engineering: A 844, 143189, 2022
152022
Age-hardening behavior guided by the multi-objective evolutionary algorithm and machine learning
R Jaafreh, UM Chaudry, K Hamad, T Abuhmed
Journal of Alloys and Compounds 893, 162104, 2022
132022
A comparative study of strain rate constitutive and machine learning models for flow behavior of AZ31-0.5 Ca Mg alloy during hot deformation
UM Chaudry, R Jaafreh, A Malik, TS Jun, K Hamad, T Abuhmed
Mathematics 10 (5), 766, 2022
112022
Brittle and ductile characteristics of intermetallic compounds in magnesium alloys: A large-scale screening guided by machine learning
R Jaafreh, YS Kang, K Hamad
Journal of Magnesium and Alloys 11 (1), 392-404, 2023
102023
Solid electrolytes for Li-ion batteries via machine learning
S Pereznieto, R Jaafreh, J Kim, K Hamad
Materials Letters 337, 133926, 2023
92023
Incorporation of machine learning in additive manufacturing: a review
A Raza, KM Deen, R Jaafreh, K Hamad, A Haider, W Haider
The International Journal of Advanced Manufacturing Technology 122 (3), 1143 …, 2022
92022
A Machine Learning‐Assisted Approach to a Rapid and Reliable Screening for Mechanically Stable Perovskite‐Based Materials
R Jaafreh, A Sharan, M Sajjad, N Singh, K Hamad
Advanced Functional Materials 33 (1), 2210374, 2023
82023
Crystal structure guided machine learning for the discovery and design of intrinsically hard materials
R Jaafreh, T Abuhmed, JG Kim, K Hamad
Journal of Materiomics 8 (3), 678-684, 2022
82022
Learning techniques for designing solid-state lithium-ion batteries with high thermomechanical stability
S Kumar, R Jaafreh, S Dutta, S Pereznieto, K Hamad, DH Yoon
Materials Letters 351, 135049, 2023
32023
Discovery of solid-state electrolytes for Na-ion batteries using machine learning
S Pereznieto, R Jaafreh, J Kim, K Hamad
Materials Letters 349, 134848, 2023
32023
Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling
R Jaafreh, JG Kim, K Hamad
Crystals 12 (9), 1247, 2022
32022
Phonon DOS-Based Machine Learning Model for Designing High-Performance Solid Electrolytes in Li-Ion Batteries
R Jaafreh, S Pereznieto, S Jeong, IP Widiantara, JM Oh, JH Kang, J Mun, ...
International Journal of Energy Research 2024, 2024
12024
Accelerated discovery of perovskite materials guided by machine learning techniques
S Kumar, S Dutta, R Jaafreh, N Singh, A Sharan, K Hamad, DH Yoon
Materials Letters 353, 135311, 2023
12023
Utilizing machine learning and phonon density of states for innovative approaches to design and optimize high-performance solid-state Mg-ion electrolytes
R Jaafreh, JG Kim, K Hamad
Journal of Power Sources 606, 234575, 2024
2024
Predictive modeling of critical temperatures in magnesium compounds using transfer learning
S Kumar, R Jaafreh, S Dutta, JH Yoo, S Pereznieto, K Hamad, DH Yoon
Journal of Magnesium and Alloys, 2024
2024
Pure Tunable Emissions from Cesium Manganese Bromide by Monitoring the Crystal Fields Through a Sustainable Approach
S Dutta, S Panchanan, S Kumar, R Jaafreh, JH Yoo, SB Kwon, K Hamad, ...
Advanced Sustainable Systems, 2400092, 0
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Articles 1–19