Welcome To KFUPM Physics

Welcome to the Department of Physics at King Fahd University of Petroleum and Minerals. I invite you to explore our website where you can find information about our academic programs, courses, research activities, and faculty members. Physics has always been at the forefront of exploration. This is accomplished through cognitive enhancement by education transmitted through academic courses as well as carrying out research at the cutting-edge frontiers of human knowledge. The Physics department offer courses that are solidly based on the American system to meet international quality assurance requirements, which has placed the Department as a world-class regional center in a leading international institution. Our student body includes pure-physics students and double-major students, which reflects the interdisciplinary nature of our program. In addition, our research facilities span various fields of physics including condensed matter physics, lasers, materials research, magnetism and superconductivity, nuclear physics, and nonlinear and computational physics. Over the past five decades, our faculty members have conducted research using in-house facilities as well as collaborative research with national and international centers.

Mohammad Al-Kuhaili,
Professor & Chairman, Physics Department

Research and Academic activity statistics 2015 to 2021

3.1

Average Impact Factor Publications​

51

Patents

10042

Cumulative Citation Count​

452

Publications

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SEMINAR

Machine Learning Prediction of Materials Properties from Chemical Composition


Speaker:
Nufida Dwi Aisyah
Regular PhD Student
Date: Monday, 16 December 2024
Time: 11:00 a.m.
Location: Bldg. 6/Room 125

Abstract:
Recently, machine learning (ML) has emerged as an additional scientific methodological approach besides theory, experiment, and computation. Although not a new concept, ML has emerged as a powerful tool in materials science, particularly for predicting material properties based on chemical composition. However, it is very common for some computer-aided applications, such as those in computational sciences and ML, to utilize ”black box” approaches, which sacrifice interpretability and may lead to computational artifacts, and erroneous conclusions. This is why, we plan to implement physics-guided ML (PGML), where interpretability is maintained to have new insights and where each feature should hold physical significance. Through this seminar, I present the implementation of PGML for predicting materials properties from chemical composition. Additionally, this will enhance the understanding of relationships between chemical composition and material properties, leading the way for more efficient material design and discovery processes.

All faculty, researchers and students are invited to attend.

    Location and Time
  • 6/125

  • 16 Dec, 2024

  • 11:00 PM - 12:00 PM