Master's program

Computational Materials Modeling

The Master of Engineering degree with a specialization in Molecular Engineering and Computational Materials Modeling provides students with advanced training in applied mathematics, thermodynamics, transport, quantum engineering, multiscale materials modeling, numerical methods, machine learning, and statistical data analysis.

The course sequence blends molecular engineering, quantum and classical simulation, and data science to provide an integrated training program for the simulation, design, and engineering of materials at scales ranging from Angstroms to meters.

In-depth domain electives permit domain specializations in biomaterials, polymer physics, quantum materials, scale-up, or experimental design.

In-depth methods electives offer specializations in data analytics, machine learning, deep learning, optimization, or visualization.

A humanities or business elective provides foundational training in ethics, epistemology, new venture start-up, or commercialization.

This integrated program of study will place students to be at the forefront of multiscale materials simulation and design, and to prepare them for careers or advanced studies in molecular engineering, materials science, chemical engineering, applied physics, polymer science, and allied fields.

Faculty leads: Juan de PabloAndrew Ferguson

(Choose 2)

  • Math Methods for Molecular Engineering (MENG 31100) – A
  • Thermodynamics and Statistical Mechanics (MENG 31200) – A

(Choose 1)

  • Biological Materials (MENG 33100) – A
  • Polymer Physics (MENG 35120) – S
  • Electronic and Quantum Materials for Technology (MENG 36600) – S
  • Transport Phenomena (MENG 31300) – A
  • Advanced Quantum Engineering (MENG 31400) – A

(Choose 3)

  • Classical Molecular and Materials Modeling (MENG 35500) – W
  • Quantum Molecular and Materials Modeling (MENG 35510) – S
  • Principles of Engineering Analysis II (MENG 21200) – W
    ~or~ Basic Numerical Analysis (MATH 21100) – S
    ~or~ Numerical Linear Algebra (STAT 24300 / STAT 30750) – A

(Choose 2)

  • Fundamentals of Deep Learning (TTIC 31230) – A
  • Introduction to Machine Learning (TTIC 31020) – A
  • Introduction to Bioinformatics and Computational Biology (TTIC 31050) – X
  • Introduction to Data Science I (CMSC 11800) – A
  • Introduction to Data Science II (CMSC 11900) – W
  • Mathematical Foundations of Machine Learning (CMSC 25300) – A
  • Applied Regression Analysis (STAT 22400) – A, S
  • Introduction to Mathematical Probability (STAT 25100) – A, S
  • Applied Linear Statistical Methods (STAT 34300) – A
  • Concepts of Programming (Immersion Programming) (MPCS 50101) – A
  • C Programming (MPCS 51040) – A
  • Python Programming (MPCS 51042) – A
  • High Performance Computing (MPCS 51087) – W
  • Time Series Analysis and Stochastic Processes (MPCS 58020) – S
  • Cloud Computing (MPCS 51083) – S
  • Big Data Application Architecture (MPCS 53014) – A
  • Bioinformatics (MPCS 56420) – A
  • Numerical Methods (MPCS 58001) – S

(Choose 1)

  • Applied Artificial Intelligence for Materials Science and Engineering (MENG 35620) – W
  • Applied Scientific Computing in Molecular Engineering (MENG 35610) – W
  • Design, Processing, and Scale-Up of Advanced Materials (MENG 35630) – S

(Choose 1)

  • Computers, Minds, Intelligence and Data (HIPS 25205) – S
  • Medical Ethics in the Hospital and Clinic (HIPS 25210) – W
  • Entrepreneurial Finance and Private Equity (BOOTH 34101) – A, W, S
  • New Venture Strategy (BOOTH 34102) – A, W, S
  • Building the New Venture (BOOTH 34103) – A, W
  • Commercializing Innovation: Tools to Research and Analyze Private Enterprises (BOOTH 34106) – A, S
  • Innovation Leadership (BOOTH 34108) – S
  • Entrepreneurial Discovery (BOOTH 34705) – A
  • Lab to Launch (BOOTH 34709) – W
  • New Products and Services (BOOTH 37200) – W
  • Lab in Developing New Products and Services (BOOTH 37201) – W
  • Technology Strategy (BOOTH 39101) – A
Autumn Quarter
  • Math Methods in Molecular Engineering (MENG 31100)
  • Thermodynamics and Statistical Mechanics (MENG 31200)
  • Introduction to Data Science I (CMSC 11800)
Winter Quarter
  • Classical Molecular and Materials Modeling (MENG 35500)
  • Medical Ethics in the Hospital and Clinic (HIPS 25210)
  • Introduction to Data Science II (CMSC 11900)
Spring Quarter
  • Quantum Molecular and Materials Modeling (MENG 35510)
  • Basic Numerical Analysis (MATH 21100)
  • Design, Processing, and Scale-Up of Advanced Materials (MENG 35630)
  • Polymer Physics (MENG 35120)
Autumn Quarter
  • Math Methods in Molecular Engineering (MENG 31100)
  • Thermodynamics and Statistical Mechanics (MENG 31200)
  • Advanced Quantum Engineering (MENG 31400)
  • Introduction to Machine Learning (TTIC 31020)
Winter Quarter
  • Classical Molecular and Materials Modeling (MENG 35500)
  • Building the New Venture (BOOTH 34103)
  • Fundamentals of Deep Learning (TTIC 31230)
Spring Quarter
  • Quantum Molecular and Materials Modeling (MENG 35510)
  • Basic Numerical Analysis (MATH 21100)
  • Design, Processing, and Scale-Up of Advanced Materials (MENG 35630)