About Abdullah Khan:
Mr. Abdullah Khan is a materials engineering researcher and academic with expertise in thin film deposition, advanced materials characterization, and data-driven materials design. His work bridges experimental materials science with computational approaches, focusing on thin film coatings, structure-property relationships, and the use of Artificial Intelligence (ANN, ML, DOE) to optimize material performance.
He has hands-on experience with Magnetron Sputtering, PVD processes, and advanced characterization techniques including AFM, XRD, SEM, and nanoindentation. His research contributions include several peer-reviewed publications on sputtered coatings, stress prediction, and functional thin films for energy applications.
Alongside his research, Mr. Abdullah has served as a Lecturer at Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI), where he taught advanced courses in surface engineering, nanotechnology, and electronic materials. He also gained academic leadership experience as Vice Principal at Abasyn Public School and College, where he worked to enhance teaching and student performance.
Driven by a passion for interdisciplinary science and innovation, Abdullah Khan continues to pursue research at the crossroads of materials science, device physics, and artificial intelligence, with applications spanning energy storage, electronics, aerospace, and biomedical engineering.
Selected Publications
- Abdullah, Ali R, Lughmani WA, Abbas SZ, Khan MA, Ahad IU. (2024) Residual stresses prediction in transition metal nitrides sputtered coatings using artificial neural network and experimental evaluation of surface morphology. Journal of Materials Research and Technology, ISSN 2238-7854. (IF 6.3) https://doi.org/10.1016/j.jmrt.2024.07.169
- Fawad, M., Ali, M., Abdullah., Ramzan, M., Karim, A., Aziz, A., Ud, Z., Ali, S., & Muhammad, K. (2023). Novel MoS 2 -sputtered NiCoMg MOFs for high-performance hybrid supercapacitor applications. Separation and Purification Technology,310(January),123101. (IF 9.2) https://doi.org/10.1016/j.seppur.2023.123101
- Faizi, T., Abdullah., & Ali, R. (2022). Effect of Bias Voltage on Adhesion and Corrosion Resistance of Titanium Nitride Coated Stainless Steel Substrate. Advanced Materials Research, 1174, 103–112. https://doi.org/10.4028/p-nf315p
Experience:
Courses Taught (Lecturer DMSE)
MM 101 Materials, Nanotechnology and Sustainability
MM 141 L Materials, Nanotechnology and Sustainability Lab
MM 214 Mineral Processing and Extractive Metallurgy
MM 233 Alloy Production and Casting Design
MM 243 L Materials Lab III
MM 344 L Materials Lab IV
MM 345 L Materials Lab V
MM 393 Electronic and Magnetic Materials
MM 447 L Materials Lab VII
MM 451 Manufacturing Processes II
Vice Principal, Abasyn Public School and College, Swat, Pakistan | Feb 2021 – Jun 2023.
As Vice Principal, Mr. Abdullah focused on strengthening the academic environment by implementing effective policies for student learning, discipline, and performance monitoring. He regularly reviewed assessment data to identify patterns and address areas where students required additional academic support. Through these initiatives, he contributed to fostering a culture of continuous improvement and academic excellence.
Graduate Research Assistant, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI), Topi, Swabi, Pakistan | 2021 – 2023.
During his tenure as a Graduate Research Assistant, Mr. Abdullah conducted experimental and computational research on magnetron-sputtered thin films, emphasizing residual stress prediction and structure–property correlations. He carried out advanced characterization using AFM, XRD, and SEM to analyze nanoscale morphology and its link to macroscopic material behavior. His research integrated data-driven approaches such as Artificial Neural Networks and Design of Experiments for process optimization. He contributed to several peer-reviewed publications and presented findings at conferences, gaining valuable experience in scientific communication and interdisciplinary collaboration.
Digital Skills:
Artificial Neural Networks (ANN) | Machine Learning | Natural Language Processing (NLP), Python | C++ | MATLAB | Design Expert (DOE) | PDAnalysis (XRD data) | Xpert HighScore | Gwyddion (AFM image processing – excellent) | Mendeley | Microsoft OfficeSEM analysis | AFM | Nanoindentation | XRD | Thin Films.
Best Performance Award (2025-26)
Mr Abdullah Khan has been recognised as the Best-Performing Employee of the Year 2025–26 by the Rector, Pro-Rector (Academics), and Pro-Rector (Administration) of the Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI). This award acknowledges his outstanding performance, dedication, and valuable contributions to the Institute.
MTME (2025 & 2026)
Below are a few glimpses from the MTME 2025 and MTME 2026 conferences, co-organised by Department of Materials Engineering and Department of Mechanical Engineering, GIK Institute.
Mr Abdullah Khan contributed to both conferences as a member of the organising team.
