In Duke’s Master of Engineering in AI + Materials, you’ll step into the fast-growing field of AI materials science and learn how data and algorithms drive new material breakthroughs. You’ll use artificial intelligence for materials science to design, test and improve advanced materials for energy, health care, electronics and more. You’ll also hold an industry internship and receive business training and 1:1 mentorship from leading faculty.
*To apply: Choose “MEng in Materials Science and Engineering” and then choose the “AI + Materials” track in the portal.
Features & Benefits of Duke’s AI Materials Science Master’s
Duke is one of the few places where you don’t have to choose between becoming an AI expert or a materials scientist—you’ll graduate fluent in both. Here, you’ll:
Build true dual fluency. Go deep in machine learning and materials science so you can speak the language of data scientists, materials engineers and business leaders alike. This is the benefit of an AI materials science MEng over a standard masters in materials science.
Get a purpose-built AI for materials science curriculum. Follow a focused plan that connects core ML, materials science and computational modeling—designed specifically for the AI materials frontier, not retrofitted from a generic program.
Add business and leadership to your toolkit. Take courses in management and high-tech industries so you’re ready to lead AI-powered materials projects, not just contribute behind the scenes.
Experience a collaborative program with baked-in networking. Duke is known for a culture of collaboration among students, faculty and industry partners. An internship or external research project in AI + materials is included, giving you portfolio-ready experience and industry connections before you graduate.
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).
I worked in Silicon Valley for five years. … When I applied to grad school, I had a guiding star of wanting to figure out how to get AI architectures to understand conceptual information [and] make sense of new data. Not only has the AI + Materials program empowered me, it has given me access to people and information such that I was able to figure out that this was even possible. I learned that this was doable here at Duke.
Han ZhangAI + Materials graduate student
Career Outcomes for the AI Materials Science MEng
AI is transforming how new materials are discovered and brought to market—and employers are racing to hire people who speak both languages. Global “materials informatics” and AI-in-materials markets are projected to grow 20%-40% annually this decade, driven by demand for advanced materials in electronics, energy, aerospace, automotive and health care. Job boards already list hundreds of thousands of roles using this skill set, from materials informatics engineer to data-driven R&D scientist.
As a Duke Engineering master’s graduate, you’ll step into that growth with a powerful track record behind you: Duke’s related AI-oriented MEng programs report strong placement rates. You’ll also have access to dedicated career coaches, employer networking and the global Duke alumni network to help you translate your AI + materials projects into your next role.
Roles for Graduates of the AI Materials Science MEng
Roles for Graduates of the AI + Materials MEng will be prepared for roles such as:
Materials informatics engineer or AI materials engineer in energy, electronics and advanced manufacturing
Computational materials scientist or simulation engineer in R&D labs
Data scientist/machine learning engineer focused on materials discovery, manufacturing or process optimization
Battery, semiconductor or electronic materials engineer in industry or startups
Product development or process engineer using AI models to design and scale new materials
You’ll also be well-positioned for Ph.D. study or research careers at national labs, research institutes and AI-for-materials centers.
Resources & Impact in AI for Materials Science
Built on Duke’s $3 million NSF research traineeship for Artificial Intelligence for Materials (aiM), you won’t be studying AI + materials in isolation. You’ll join an interdisciplinary community of engineers, computer scientists and industry partners who are rethinking how new materials are discovered and deployed. Expect hands-on workshops, collaborative projects and a culture that pushes you to turn ideas into impact. You’ll have more opportunities for collaboration than at many masters in materials science programs.
You’ll work closely with highly cited faculty who are defining the future of computational materials and AI-driven discovery. From advanced characterization tools to high-performance computing resources, Duke Engineering gives you access to the facilities you need to test ideas, iterate quickly and build a portfolio that stands out to employers and Ph.D. programs. Leverage facilities like the Duke Compute Cluster and the Shared Materials Instrumentation Facility (SMIF), and benefit from Duke Engineering’s broader research strength.
Explore how AI + materials can accelerate breakthroughs in clean energy, resilient infrastructure, biomedical devices, semiconductors and more. By graduation, you’ll have experience applying your skills to problems that matter—ready to contribute on day one in industry, startups or research labs.
The two programs share a strong foundation in fundamentals of artificial intelligence. However, they differ in application focus. The AIPI program is aimed at students who want to broadly build and deploy AI-powered products, software applications and full-stack machine learning systems for products and services across industries.
The AI + Materials program, offered in the Pratt School of Engineering through the Thomas Lord Department of Mechanical Engineering and Materials Science, focuses on using AI and machine learning to accelerate materials discovery, processing and creation. You’ll learn how to apply modern AI tools to engineer next-gen materials for energy, infrastructure, electronics and more.
Before pursuing a masters in materials science, you should have a strong technical background. However, you do not need extensive AI and ML experience for admission into the program. Students typically come from engineering, computer science, physics, chemistry, math or related STEM fields.
You should feel comfortable with programming fundamentals and problem-solving. However, we’ve structured our curriculum to support students who are new to applied AI and even new to Python. The degree includes foundational courses to ensure you quickly build the skills needed for more advanced work. Take advantage of these offerings to fill the gaps in your educational background.
We emphasize experiential learning so that you will contribute to actual materials research and computational modeling projects. Internships are especially valuable, as you will gain exposure to real-world processes and workflows related to the field of materials science. Hands-on learning is one of the best ways to master AI materials science concepts.
AI + Materials graduates pursue careers in R&D, materials engineering, and AI and data science roles in many industries. They also possess the skills required to step into specialties such as computational modeling, manufacturing process optimization, battery material discovery and semiconductor design. This AI materials science MEng is a new program, but Duke’s related AI-oriented MEng programs report very strong placement rates.
Take the Next Step With Duke’s AI + Materials MEng
We look forward to sharing more about the program and being a part of your journey. If you’d like to learn more about Duke’s AI materials science MEng, contact us or apply today. Applicants for fall 2026 admission should choose MEng in Materials Science and Engineering in the portal and select the AI + Materials track.
