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About the College

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  • About the College
  • Faculty
  • Advanced Materials Science and Engineering

Faculty

  • Associate Professor Mechanical Metallurgy & Automated Materials Design and Discovery
  • KOTIBA, HAMAD 홈페이지 바로가기
    Lab Material's Automated Design & Discovery (MADD)

Education

  • (2012) PhD, Applied Chemistry, Department of Chemistry, Damascus University, Syria.
  • (2008) Master, Applied Chemistry, Department of Chemistry, Damascus University, Syria.
  • (2006) High Diploma, General Chemistry, Department of Chemistry, Damascus University, Syria.
  • (2005) Bachelors, Pure Chemistry, Department of Chemistry, Damascus University, Syria.

Experience

  • Postdoc researcher at School of materials science & engineering, Yeungnam University (South Korea). (Mar. 1st 2013 - Aug. 31st 2015)

Journal Articles

  • (2024)  Phonon DOS-Based Machine Learning Model for Designing High-Performance Solid Electrolytes in Li-Ion Batteries.  INTERNATIONAL JOURNAL OF ENERGY RESEARCH.  2024,  2138847
  • (2023)  Accelerated discovery of perovskite materials guided by machine learning techniques.  MATERIALS LETTERS.  353,  135311
  • (2023)  Texture Transformation Induced Grain Fragmentation.  METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE.  54,  12
  • (2023)  Effect of CaO Content and Annealing Treatment on the Room-Temperature Mechanical Properties of AZ61 and AZ61-CaO Alloys.  METALS.  13,  12
  • (2023)  Achieving high strength and ductility of multi-phase steel via hub-border architecture formed in 30 s.  JOURNAL OF ALLOYS AND COMPOUNDS.  972,  1
  • (2023)  Learning techniques for designing solid-state lithium-ion batteries with high thermomechanical stability.  MATERIALS LETTERS.  351,  1
  • (2023)  Discovery of solid-state electrolytes for Na-ion batteries using machine learning.  MATERIALS LETTERS.  349,  15
  • (2023)  Solid electrolytes for Li-ion batteries via machine learning.  MATERIALS LETTERS.  338,  1
  • (2022)  A Machine Learning-Assisted Approach to a Rapid and Reliable Screening for Mechanically Stable Perovskite-Based Materials.  ADVANCED FUNCTIONAL MATERIALS.  33,  1
  • (2022)  Investigating the Microstructure, Crystallographic Texture and Mechanical Behavior of Hot-Rolled Pure Mg and Mg-2Al-1Zn-1Ca Alloy.  CRYSTALS.  12,  10
  • (2022)  Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling.  CRYSTALS.  12,  9
  • (2022)  Poly(butylene succinate) (PBS): Materials, processing, and industrial applications.  PROGRESS IN POLYMER SCIENCE.  132,  1
  • (2022)  Effect of deformation temperature on the slip activity in pure Mg and AZX211.  JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T.  19,  -
  • (2022)  Brittle and ductile characteristics of intermetallic compounds in magnesium alloys: A large-scale screening guided by machine learning.  JOURNAL OF MAGNESIUM AND ALLOYS.  6,  1
  • (2022)  paper Crystal structure guided machine learning for the discovery and design of intrinsically hard materials.  JOURNAL OF MATERIOMICS.  8,  3
  • (2022)  A deep learning perspective into the figure-of-merit of thermoelectric materials.  MATERIALS LETTERS.  319,  1
  • (2022)  Effect of CaO on structure and properties of AZ61 magnesium alloy.  MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING.  844,  143189
  • (2022)  A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation.  MATHEMATICS.  10,  5
  • (2022)  Corrosion behavior of AZ31 magnesium alloy with calcium addition.  CORROSION SCIENCE.  199,  1
  • (2022)  Age-hardening behavior guided by the multi-objective evolutionary algorithm and machine learning.  JOURNAL OF ALLOYS AND COMPOUNDS.  893,  -