Use AI to discover what’s next in materials science and engineering. Work alongside faculty and peers across engineering, science, medicine and data science to tackle complex materials problems.
*Note: In the application portal, choose “MEng in Materials Science and Engineering” and choose the AI+Materials track!
Duke’s Master of Engineering in AI + Materials specializes in AI-driven materials design, empowering you to imagine—and create—the technologies of tomorrow: from next-generation energy systems and resilient urban infrastructure to personalized healthcare solutions.
With 30 credits, you’ll merge advanced technical coursework with hands‑on, industry‑relevant experience, mastering artificial intelligence and machine learning techniques to tackle real‑world materials challenges.
These students are truly dually fluent in AI algorithms and methods and in the fundamentals of materials science.
Cate BrinsonSharon C. and Harold L. Yoh, III Distinguished Professor of Mechanical Engineering and Materials Science
Duke Engineering Launches Master’s Program Blending AI and Materials Science
Duke’s masters program in AI + Materials is leading the way, preparing students to lead in companies, start-ups and research. Inventing and optimizing materials that enable radical advances in energy, bio-implants, manufacturing and more.
This master’s degree is 30 credits, taken over three semesters on the Duke campus.
Required:
MENG 540: Management of High-Tech Industries
MENG 570: Business Fundamentals for Engineers
MENG 550/551: Internship and Internship assessment
Select one:
COMPSCI 526 Data Science
STA 521L: Predictive Modeling and Statistical Learning
STA 522L: Study Design: Design of Surveys and Causal Studies
STA 523L: Programming for Statistical Science
STA 611: Introduction to Mathematical Statistics
*If a student has sufficient programming or stats undergraduate course experience, they may waive this requirement and take an additional ML or materials course instead.
Introduction to Machine Learning (select 1):
ME 555-09/CEE 690 Data Science and machine learning for applied science and engineering
CS 570: Artificial Intelligence
ECE 580: Introduction to Machine Learning
ECE 682D/STA 561D: Probabilistic Machine Learning
Advanced Machine Learning (select 1):
ECE 685D/COMPSCI 675D: Introduction to Deep Learning
COMPSCI 527 Introduction to Computer Vision
ECE 590: Advanced Deep Learning
ECE 590: Computer Engineering ML and Deep Neural Nets
COMPSCI 570 Artificial Intelligence
COMPSCI 661
COMPSCI 671D:Theory and Algorithms for Machine Learning
COMPSCI 572 Introduction to Natural Language Processing
Select two:
CEE 520: Continuum Mechanics
CEE 521: Elasticity
CHEM 548: Solid-State/Materials Chemistry
CHEM 590: Polymer Synthesis
ECE 524: Introduction to Solid-State Physics
ECE 511: Foundations of Nanoscale Science & Technology
ME 510: Diffraction and Spectroscopy
ME 512: Modern Materials
ME 514: Theoretical and Applied Polymer Science
ME 515: Electronic Materials
ME 519: Molecular Modeling of Soft Materials
ME 555 Intermediate Polymer Physics
ME 555: Intro to Rheology
ME 562: Materials Synthesis and Processing
ME 563: Fundamentals of Soft Matter
ME 564: Introduction to Polymer Physics
PHYS 516: Quantum Materials
Other materials-related courses may be substituted for the above list with pre-approval.
Select one:
***ME 582: Data and Materials Science Applications
ME 555: Numerical Optimization
CEE-628: Uncertainty Quantification
ME 511: Computational Materials Science
ME 524: Introduction to the Finite Element Method
ME 525: Nonlinear Finite Element Analysis
ME 519: Molecular Modeling of Soft Materials
ME 555: Multiscale Methods
ME 555: Sci Computing, Simulation and ML
*** Strongly encouraged
Internship or external research experience related to machine learning and materials science (approved as 3-4 unit course by DGS).
