Research Stories
AI for Understanding and Characterizing the Ductile-Brittle Behaviors of Mg-based Materials
Investigate the applicability and potential of AI technology in material discovery and design
Advanced Materials Science and Engineering
Prof.
KOTIBA, HAMAD
Russlan Jaafreh · Yoo Seong Kang · Santiago Pereznieto
On June 11th, the research team led by Prof. Kotiba Hamad at the school of advanced materials science and engineering (AMSE) published a paper titled “Brittle and ductile characteristics of intermetallic compounds in magnesium alloys: A large-scale screening guided by machine learning” in the Journal of Magnesium and Alloys (IF =11.8) which is ranked the 1st in the category of metallurgy & metallurgical engineering according to Clarivate’s Journal Citation Reports’ (JCR) ranking. This study is one of the works conducted by this group to investigate the applicability and the potential of AI techniques in the field of materials discovery and design. The findings of this work showed that, by machine learning (ML), a technique of AI, the brittle-ductile characteristics of intermetallic compounds that form in magnesium-based alloys are reliably, accurately, and quickly predicted. The ML results were validated by theoretical calculations done by density functional theory (DFT), shown in the figure below. The results can facilitate the designing of magnesium alloys with high performance for structural applications.
This led to say that, due to the exploding computational capabilities, artificial intelligence, in its machine learning subcategory, has been utilized heavily in the field of material discovery and design for its ability to construct data-driven models that are magnitude faster than conventional experimentation or even physics-driven modeling and simulation. The present research group; Kotiba Hamad (Professor), Russlan Jaafreh (Ph.D. candidate), Yoo Seong Kang (Graduate collaborator/Currently working in ‘Computer Systems and Intelligence Laboratory’), and Santiago Pereznieto (Masters Student), have been utilizing the capabilities of AI in the field of material science & engineering, and have published multiple papers regarding this topic in high-tier journals such as ACS Applied Materials & Interfaces, Journal of Materiomics and many more.
Related Links:
-Russlan Jaafreh, Yoo Seong Kang, Kotiba Hamad, Journal of Magnesium and Alloys 2022, DOI: doi.org/10.1016/j.jma.2022.05.006.
-Russlan Jaafreh, Yoo Seong Kang, and Kotiba Hamad, ACS Applied Materials & Interfaces 2021 13 (48), 57204-57213, DOI: doi.org/10.1021/acsami.1c17378
-Professor Kotiba’s Website: kotibahamad995.wixsite.com/aem-skku
