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 Computational Materials & Modeling (MX)

Professional Master of Computational Materials and Modeling

Admission Requirements

Students with a BS degree in Materials Science, Physics, Chemistry, Mechanical Engineering, Electrical Engineering, Chemical Engineering, Civil Engineering, or Mathematics and Statistics with a GPA of 2.5 or better are eligible to apply for the program. Students with a BS degree in other disciplines must show basic proficiency in thermal physics as well as mathematical and computational methods in science and engineering. Other degrees in science and engineering will be considered on a case-by-case basis following a review of the Program Committee.

In addition to above, the university general requirements are:

1.  Grade point average (GPA) of 2.5 or higher on a 4.0 scale

2.   Completion of TOEFL with a minimum score of 520 (PBT), 190 (CBT), or 70 (IBT).  IELTS is also acceptable

      with a minimum score of 6.0. KFUPM students are exempt from this requirement.

3. Two letters of recommendation

Satisfying the minimum admission requirements does not guarantee admission into the program, as final admission is subject to an evaluation of the entire application, and the personal interview. Based on the assessment of the applicant’s file and the personal interview, the admission committee might offer conditional acceptance for students who need to take deficiency courses.

Program Educational Objectives

  1. prepare graduates for successful academic careers in computational materials and modeling and related fields
  2. prepare graduates for successful careers in industry and research laboratories
  3. provide graduates with a broad knowledge that enables them to be self-learners

Program Learning Outcomes

On successful completion of this program, graduates will be able to:


K1. Recognize the materials major structures at the graduate level

K2. Recognize the computational tools, approaches, and features thereof at the graduate level

K3. Recognize the multiscale structure-property relationships at the graduate level


S1. Atomistic modeling of material structures and properties at the graduate level

S2. Numerical simulation of materials using finite elements and multiscale modeling at the graduate level

S3. Selection of proper materials and design of novel ones

S4. Analysis and interpretation of computational data and writing concise reports


C1. Be a good and ethically responsible team player

C2. Use numerical skills to solve problems in materials at the graduate level

C3. Use computing tools to solve problems in materials at the graduate level

C4. Search and utilize information on topics in computational materials from a variety of sources

C5. Communicate material science concepts verbally, graphically, and in writing

C6. Setup and conduct computational investigations in order to understand, select, and design


Course Descriptions

MSE 500 Survey of Materials Science and Engineering (3-0-3)

This course surveys Materials Science and Engineering at a beginning graduate level for students whose undergraduate degree was not in Materials Science and Engineering.  Review of bonding, crystal structure, defects, diffusion, mechanical properties, annealing, solidification, phase equilibria, strengthening mechanisms. Survey of engineering materials: metallic alloys, ceramics, polymers, composites, and construction materials. Physical properties: electrical, thermal, magnetic, and optical. Focuses on micro/nanostructure and its manipulation in order to control materials properties. 

Pre-requisites: Graduate Standing

MSE 502 Thermodynamics in Materials Science 3-0-3

Classical and irreversible thermodynamics, phase equilibria, theory of solutions, surface phenomena, thermodynamics and kinetics of chemical reactions, electrochemistry, gas-solid reactions.

Pre-requisites: Graduate Standing

MATH 578 Applied Numerical Methods II 3-0-3

This course introduces finite element, finite difference and finite volume methods, applications to steady-state, diffusion and wave models. Stability and convergence. Homogenization, upscale and multiscale methods. Implementations and computer lab sessions.

Pre-requisites: Graduate Standing

PHYS 531 Monte Carlo Simulations in Statistical Mechanics 3-0-3

Review of pertinent topics in classical and quantum physics. Gibb’s statistical ensembles, MB, BE, and FD statistics with simple applications to solids. Theoretical foundations of Monte Carlo simulation, Markov chains, random walks. Study of phase transitions in the 2D and 3D Ising models as well as in the Landau Ginsburg Model using Monte Carlo simulations. Brownian Dynamics as an example of simulation for the study of stochastic systems.

Pre-requisites: Graduate Standing

MSE 549 Introduction to Atomistic Simulations 3-0-3

Introduction to atomistic simulations covers both classical and quantum mechanics techniques. The course is primarily hands-on with a very brief introduction to essential statistical thermodynamics and quantum mechanics concepts. The main focus of the class is on classical molecular dynamics and density functional theory. Basic shell scripting will be introduced as efficient computer simulations relay on some scripting abilities. 

Pre-requisites: Graduate Standing

CHEM 501 Physical Chemistry : Molecular Approach 3-0-3

The course will cover two chapters in each of three areas of physical chemistry in the assigned course textbook; namely, quantum chemistry, spectroscopy, and statistical thermodynamics.

Pre-requisites: Graduate Standing

PHYS 574 Multiscale Material Design 3-0-3

This course is to present the theories and methods in multiscale modeling and simulations of materials, both in multi-length and multi-time scales. It covers the algorithmic basis for atomic scale, mesoscale and continuum scale modeling approaches, emphasizing the atomic-to-continuum connection and homogenization problems in continuum modeling of materials. Concrete examples will be used to explain the basic knowledge about the principles, concepts, methods, tools, and packages in multiscale modeling and design. Students will have hands-on experience on the applications of multiscale modeling and design on solid materials, fluids, and soft materials.

Pre-requisites: MSE 549, MATH 578

PHYS 573 Materials Informatics 3-0-3

The course provides an introduction to materials informatics, which is an intersection between materials science, computational methods, and big-data sciences. The emphasis will be toward foundational backgrounds including an introduction to machine and statistical learning, ML-based materials science modeling, and implementations. As the fielding is expanding, a short overview of the contemporary trends in the field will be provided. 

Pre-requisites: Graduate Standing

PHYS 619 Project (0-0-6)

A graduate student will arrange with a faculty member to conduct an industrial research project related to the MX program in Physics Department. Subsequently the students shall acquire skills and gain experiences in developing and running actual industry-based project. This project culminates in the writing of a technical report, and an oral technical presentation in front of a board of professors and industry experts.

Pre-requisites: Graduate Standing

Elective Courses

MSE 534    Composite Materials (3-0-3)

PHYS 576   First-principles Calculations of Materials (3-0-3)

ME 577      Deformation, Fatigue and Fracture of Engineering Materials (3-0-3)

STAT 503   Probability and Statistics for Data Science (3-0-3)

EM 550      Engineering Project Management (3-0-3)

EE 546       Semiconductor Device Theory (3-0-3)

CHE 543    Polymeric Materials (3-0-3)

MSE 507    Kinetics, Diffusion, and Phase Transformations (3-0-3)

PHYS 532   Solid State Physics (3-0-3)

CHEM 560 Energy: Materials and Processes (3-0-3)

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