In this track, you will prepare for the simulation, design, and engineering of materials at scales ranging from Angstroms to meters. With training in artificial intelligence and numerical methods, you’ll be prepared for multiscale modeling using classical, quantum, and data-driven techniques for molecular and materials simulation, evaluation, optimization, and design.
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.
This track replaces the former Computational Modeling of Materials track, which you can find archived here.
- 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
- (Track Core) MENG 35640: AI and Materials Development
- Electronic and Quantum Materials for Technology (MENG 36600) - Autumn
- OMICs Technologies and Applications in Biological Systems (MENG 33130) - Autumn
- Design, Processing, and Scale-Up of Advanced Materials (MENG 35630) - Spring
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. Additionally, enrollment in advanced MPCS courses requires taking a placement exam, which is scheduled before the quarter begins.
- Fundamentals of Deep Learning (TTIC 31230)
- Introduction to Machine Learning (TTIC 31020)
- Introduction to Bioinformatics and Computational Biology (TTIC 31050)
- Mathematical Foundations of Machine Learning (CMSC 25300)
- Concepts of Programming (MPCS 50101)
- C Programming (MPCS 51040)
- Python Programming (MPCS 51042)
- Intermediate Python Programming (MPCS 51046)
- Advanced Programming (MPCS 51100)
- Time Series Analysis and Stochastic Processes (MPCS 58020)
- Bioinformatics (MPCS 56420)