To understand, evaluate, and design materials often requires computational techniques at the forefront of multiscale materials simulation and design.
In this track, you will prepare for the simulation, design, and engineering of materials at scales ranging from Angstroms to meters. You will learn fundamentals of thermodynamics, transport, and quantum engineering. With these and training in applied mathematics and numerical methods, you’ll be prepared for multiscale material modeling, and both classical and quantum simulation.
In-depth electives allow you to specialize in biomaterials, polymer physics, quantum materials, scale-up, or experimental design. Other electives allow for the study of data analytics, machine learning, deep learning, optimization, or visualization.
This track suits candidates interested in: a career or advanced studies in molecular engineering, materials science, chemical engineering, applied physics, polymer science, and allied fields.
- Innovation Leadership Workshop: Communication and Negotiation with Individuals, Teams, and Organizations
- (Innovation Leadership) MENG 30000: Introduction to Emerging Technologies
- (Track Core) MENG 31200: Thermodynamics and Statistical Mechanics
- 1 Track Elective
- (Innovation Leadership) MENG 30500: Responsible and Effective Technology Management
- (Track Core) MENG 35500: Classical Molecular and Materials Modeling
- 1 Track Elective
- (Innovation Leadership) MENG 20400: Commercializing Products with Molecular Engineering
- (Track Core) MENG 35510: Quantum Molecular and Materials Modeling
- 1 Track Elective
NOTE: Electives offered outside of PME are scheduled at the discretion of the home department. Typically they will be confirmed as available a few weeks before registration.
Track electives
- Principles of Engineering Analysis II (MENG 21200) - Winter
- Basic Numerical Analysis (MATH 21100) - Spring
- Numerical Linear Algebra (STAT 24300 / STAT 30750) - Spring
- Design, Processing, and Scale-Up of Advanced Materials (MENG 35630) – Spring
- Fundamentals of Deep Learning (TTIC 31230)
- Introduction to Machine Learning (TTIC 31020)
- Introduction to Bioinformatics and Computational Biology (TTIC 31050)
- Introduction to Data Science I (CMSC 11800)
- Introduction to Data Science II (CMSC 11900)
- Mathematical Foundations of Machine Learning (CMSC 25300)
- Applied Regression Analysis (STAT 22400)
- Introduction to Mathematical Probability (STAT 25100)
- Applied Linear Statistical Methods (STAT 34300)
- Concepts of Programming (Immersion Programming) (MPCS 50101)
- C Programming (MPCS 51040)
- Python Programming (MPCS 51042)
- Intermediate Python Programming (MPCS 51046)
- Advanced Programming (taught in C) (MPCS 51100)
- High Performance Computing (MPCS 51087)
- Time Series Analysis and Stochastic Processes (MPCS 58020)
- Cloud Computing (MPCS 51083)
- Big Data Application Architecture (MPCS 53014)
- Bioinformatics (MPCS 56420)
- Numerical Methods (MPCS 58001)