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Material's Automated Discovery (MAD)
Norwegian University of Science and Technology (NTNU)

Material's Automated Discovery (MAD) Lab

We advance materials discovery using automation, machine learning, and data-driven approaches at NTNU.

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Professor

Professor Kotiba Hamad
Kotiba Hamad
Full Professor, Department of Mechanical and Industrial Engineering, NTNU
Email: kotiba.hamad@ntnu.no

Bio Details

  • Birthdate: December 15, 1982
  • Nationality: S. Korea
  • Current Position: Full Professor, NTNU
  • Research Interests: AI for Materials Science & Engineering

Education

  • 2012: PhD, Applied Chemistry, Damascus University (Syria)
  • 2008: Master, Applied Chemistry, Damascus University (Syria)
  • 2005: Bachelors, Pure Chemistry, Damascus University (Syria)

Experience

  • 2013–2015: Postdoc, School of Materials Science & Engineering, Yeungnam University (S. Korea)
  • 2015–2023: Assistant Professor, School of Advanced Materials Science & Engineering, Sungkyunkwan University (S. Korea)
  • 2023–2025: Associate Professor, School of Advanced Materials Science & Engineering, Sungkyunkwan University (S. Korea)

Current Members

Ramzi Alnubani
Ramzi Alnubani
PhD student, NTNU
Machine-driven data mining for energy related applications
Nithusan
Nithusan Kukaraja
Master's Candidate, NTNU
AI for Li ion batteries materials

Alumni

Umer
Umer Masood Chaudry
PhD Alumni
Postdoc: University of Leicester
Russlan
Russlan Jaafreh
PhD Alumni
AI engineer: Samsung SDI, S. Korea
Surjeet
Surjeet Kuma
PhD Alumni
AI engineer: PLAIF, S. Korea
Santiago
Santiago Pereznieto
Master Alumni
AI engineer: Autosilicon, S. Korea
Kang
Kang Yoo Sung
Undergraduate Alumni
PhD student: Sungkyunkwan University, S. Korea
Choi
Choi Yong Seok
Undergraduate Alumni
Sungkyunkwan University, S. Korea

Teaching

Courses

EAM2011
Advanced Materials Process Engineering
Advanced Materials Process Engineering is an undergraduate-level course that focuses on the principles and techniques of processing engineering for metals and materials. Key topics include: Casting processes – solidification, mold design, and defect control; Powder metallurgy – powder preparation, compaction, and sintering; Plastic deformation processes – rolling, forging, extrusion, and their effects on microstructure and mechanical properties; Composites – processing methods, reinforcement selection, and property optimization. The course emphasizes understanding how processing affects material structure and properties, preparing students to design and optimize industrial material processes. I taught Advanced Materials Process Engineering for 10 years at Sungkyunkwan University (SKKU), South Korea, providing students with both fundamental knowledge and practical skills in modern materials processing.
EAM3002
Advanced Mechanical Metallurgy
Advanced Mechanical Metallurgy is an advanced course that examines the relationship between the microstructure, processing, and mechanical properties of metals and alloys. Key topics include: Crystal structures and defects, dislocations, and their impact on material behavior; Mechanical properties, including elasticity, plasticity, creep, fatigue, and fracture; Strengthening mechanisms, such as work hardening, solid solution and precipitation strengthening, and grain boundary effects. The course emphasizes linking theory to engineering applications, enabling students to predict and engineer the mechanical performance of metallic materials. I taught Advanced Mechanical Metallurgy for 10 years at Sungkyunkwan University (SKKU), South Korea, guiding students in both fundamental principles and practical problem-solving in metallurgy and materials engineering.
EAM1001
Introduction to Materials Engineering
Introduction to Materials Engineering is a fundamental course that explores the relationships between the structure, properties, processing, and performance of materials. Key topics include: Atomic structure and bonding, crystal structures, and defects in solids; Mechanical, thermal, electrical, and magnetic properties of materials; Phase diagrams, diffusion, and phase transformations; Classification and applications of metals, ceramics, polymers, and composites. The course emphasizes understanding how atomic- and micro-scale structures influence macroscopic material behavior and performance in engineering applications. It provides the essential foundation for advanced studies in materials science and engineering. I have taught Introduction to Materials Engineering to undergraduate students at Sungkyunkwan University (SKKU), South Korea, guiding them in developing a solid understanding of material behavior and its role in modern engineering design and technology.
EAM3003
Phase Transformation
Phase Transformation is an advanced course that explores the thermodynamic and kinetic principles governing changes in the structure and phases of materials. Key topics include: thermodynamics of phase equilibria, diffusion mechanisms and transformation kinetics, nucleation and growth processes, and solid-state transformations such as precipitation, martensitic, and eutectoid reactions. The course emphasizes understanding how phase transformations control microstructural evolution and influence the mechanical, thermal, and functional properties of materials. It provides the essential foundation for designing and processing materials with desired structures and performance. I have taught Phase Transformation to undergraduate and graduate students at Sungkyunkwan University (SKKU), guiding them to understand the relationship between phase transformation mechanisms and the development of advanced engineering materials.
EAM2001
Thermodynamics
Thermodynamics is a core course that provides a fundamental understanding of the principles governing energy, heat, and work in materials and engineering systems. Key topics include: the laws of thermodynamics, state functions, phase equilibria, chemical potentials, and thermodynamic relationships relevant to materials behavior and processing. The course emphasizes the application of thermodynamic principles to predict phase stability, reactions, and transformations in metals, ceramics, polymers, and composites. It equips students with the analytical tools needed to connect energy concepts with materials design and performance. I have taught Thermodynamics to both undergraduate and graduate students at Sungkyunkwan University (SKKU), helping them develop a strong theoretical and practical understanding of how thermodynamics governs material properties and engineering processes.
EAM2001
Heat Transfer
Heat Transfer is a fundamental course that examines the principles and mechanisms by which thermal energy is transmitted through different media. Key topics include: conduction, convection, and radiation heat transfer; transient and steady-state heat conduction; convective heat transfer in laminar and turbulent flows; and heat exchangers and thermal system design. The course emphasizes applying analytical and numerical methods to solve practical engineering problems involving thermal management and energy conversion. It provides students with the theoretical foundation and problem-solving skills needed to design and optimize materials and systems for effective heat control. I have taught Heat Transfer to undergraduate students at Sungkyunkwan University (SKKU), helping them build a strong understanding of thermal phenomena and their applications in materials processing and engineering design.

Teaching Awards

Teaching Award 1
Teaching Award 2

Over the years, I have been consistently recognized for excellence in teaching and student mentorship at the School of Advanced Materials Science and Engineering, Sungkyunkwan University (SKKU). I was honored as the 1st-ranked professor in educational performance in 2018, 2019, 2021, 2022, and 2023, the 2nd-ranked in 2024, and the 3rd-ranked in 2020. These awards reflect my continuous commitment to high-quality instruction, student engagement, and the development of innovative learning methods that inspire academic and professional growth.

Teaching Videos

To enhance accessibility and engagement, we provide recorded lectures and demonstration videos that help students visualize complex materials concepts and experimental techniques.

Research

Advanced materials processing

AI-Guided Materials Discovery

Our research focuses on accelerating the discovery and design of advanced materials through the power of Artificial Intelligence (AI) and data-driven modeling. Traditional materials development is often a slow and resource-intensive process. AI offers a transformative approach by enabling rapid prediction, optimization, and understanding of material properties before experimental synthesis.

In this activity, we apply machine learning, deep learning, and computational modeling to explore a wide range of material systems, including aluminum alloys, energy storage materials for metal-ion batteries, and high-performance perovskites for solar energy harvesting, as well as superhard materials.

By integrating AI with high-throughput simulations and experimental data, we aim to uncover new compositions, predict structure–property relationships, and guide the rational design of next-generation materials for sustainable technologies.

Our goal is to build intelligent frameworks that not only accelerate discovery but also enhance interpretability and innovation in materials science, driving breakthroughs across energy, structural, and functional material domains.

Computational materials science

AI-Driven Scientific Text Mining

Our research leverages Artificial Intelligence, particularly Large Language Models (LLMs), to extract and organize scientific knowledge from the vast body of published literature. Traditional literature review and data collection are time-consuming and prone to oversight; AI-driven text mining enables rapid, accurate extraction of critical insights, trends, and material-specific data.

We have applied this approach to magnesium alloys and are currently focused on building FAIR (Findable, Accessible, Interoperable, Reusable) datasets for hydrogen embrittlement, aluminum alloys processing and casting, and many other material systems.

By combining AI with structured data curation, we aim to create high-quality, standardized datasets that accelerate materials discovery, enable data-driven modeling, and support reproducible research across the materials science community.

Collaboration

Global university collaboration

We have collaborated with more than 20 universities across the world, fostering strong international partnerships that advance innovation in materials science and artificial intelligence.

Selected Publications

2025

  • Chaudry, Umer Masood; Rehan Tariq, Hafiz Muhammad; Hamad, Kotiba; Khan, Muhammad Kashif; Jun, Tea-Sung. Twinning-induced texture weakening in Mg alloy and its consequent influence on ductility and formability. Materials Science and Technology, 41(2), 101–105, 2025. SAGE Publications, London, England.
  • Jaafreh, Russlan; Kumar, Surjeet; Hamad, Kotiba; Kim, Jung-Gu. Introducing Materials Fingerprint (MatPrint): A novel method in graphical material representation and features compression. Computational Materials Science, 246, 113444, 2025. Elsevier.
  • Alzamer, Haneen; Jaafreh, Russlan; Kim, Jung-Gu; Hamad, Kotiba. Artificial Intelligence and Li Ion Batteries: Basics and Breakthroughs in Electrolyte Materials Discovery. Crystals, 15(2), 114, 2025. MDPI.
  • Chaudry, Umer Masood; Farooq, Ameeq; Sufyan, Muhammad; Tariq, Hafiz Muhammad Rehan; Malik, Abdul; Kim, Minki; Tariq, Ali; Hamad, Kotiba; Jun, Tea-Sung. Corrosion behavior of AZ31 and AZ31–0.5 Ca in different concentrations of NaCl and Na₂SO₄ at various temperatures. Corrosion, 81(3), 232–244, 2025. Association for Materials Protection and Performance.

2024

  • Mahendradhany, A.P.; Park, K.S.; Widiantara, I.P.; Kim, Min Jun; Oh, J.M.; Kang, J.H.; Hamad, K.; Ko, Y.G. Achieving high strength and ductility of multi-phase steel via hub-border architecture formed in 30 s. Journal of Alloys and Compounds, 972, 172774, 2024. Elsevier.
  • Jaafreh, Russlan; Pereznieto, Santiago; Jeong, Seonghun; Widiantara, I. Putu; Oh, Jeong Moo; Kang, Jee-Hyun; Mun, Junyoung; Ko, Young Gun; Kim, Jung-Gu; Hamad, Kotiba. Phonon DOS‐Based Machine Learning Model for Designing High‐Performance Solid Electrolytes in Li‐Ion Batteries. International Journal of Energy Research, 2024(1), 2138847, 2024. Hindawi.
  • Choi, Hyung Wook; Kim, Jiwon; Bang, Hyeon-Seok; Badawy, Khaled; Lee, Ui Young; Jeong, Dong In; Kim, Yeseul; Hamad, Kotiba; Kang, Bong Kyun; Weon, Byung Mook. Tracking accelerated oxygen evolution reaction enabled by explosive reconstruction of active species based on CoxN@NC. Journal of Materials Chemistry A, 12(12), 7067–7079, 2024. Royal Society of Chemistry.
  • Jaafreh, Russlan; Kim, Jung-Gu; Hamad, Kotiba. Utilizing machine learning and phonon density of states for innovative approaches to design and optimize high-performance solid-state Mg-ion electrolytes. Journal of Power Sources, 606, 234575, 2024. Elsevier.
  • Kumar, Surjeet; Jaafreh, Russlan; Dutta, Subhajit; Yoo, Jung Hyeon; Pereznieto, Santiago; Hamad, Kotiba; Yoon, Dae Ho. Predictive modeling of critical temperatures in magnesium compounds using transfer learning. Journal of Magnesium and Alloys, 12(4), 1540–1553, 2024. Elsevier.
  • Dutta, Subhajit; Panchanan, Swagata; Kumar, Surjeet; Jaafreh, Russlan; Yoo, Jung Hyeon; Kwon, Seok Bin; Hamad, Kotiba; Dastgeer, Ghulam; Yoon, Dae Ho. Pure Tunable Emissions from Cesium Manganese Bromide by Monitoring the Crystal Fields Through a Sustainable Approach. Advanced Sustainable Systems, 2024. Wiley.
  • Kumar, Surjeet; Jaafreh, Russlan; Singh, Nirpendra; Hamad, Kotiba; Yoon, Dae Ho. Introducing MagBERT: A language model for magnesium textual data mining and analysis. Journal of Magnesium and Alloys, 12(8), 3216–3228, 2024. Elsevier.
  • Gurjar, Kuldeep; Kumar, Surjeet; Bhavsar, Arnav; Hamad, Kotiba. An Explainable Deep Learning-Based Classification Method for Facial Image Quality Assessment. Journal of Information Processing Systems, 20(4), 2024.

Machine Learning Codes

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