TRANSFORM MATERIALS SCIENCE WITH AI
In most every realm of scientific exploration, remarkable strides have been made at the nexus of artificial intelligence and data science. Materials science is no different, where these approaches are turning the stuff of comic book lore into real-world solutions.
Duke University received a $3 million grant from the National Science Foundation to develop the aiM-NRT fellowship and AI for Materials certificate program. Fellowships are available for graduate students training for using AI for materials science research, filling the workforce gap in this emerging field. More about aiM »
At the center of this sprawling frontier lies massive data repositories–vast treasure troves of clues leading to new materials that span an awe-inspiring array of applications, like aerospace components and adaptable electronics.
The synergy between AI and materials science is transforming how we perceive and harness the infinite possibilities that the field has to offer. With this graduate certificate, you too can transform and harness that same power to contribute to a sustainable future.
Carve your path in this AI frontier. Contact us.
“Duke’s aiM Certificate program trains students to be ‘natives’ in AI and materials, equipping them to dramatically accelerate materials design for applications of societal impact. For example, these materials will enable next generation energy systems, the development of more resilient roads and bridges, and access to higher-quality, personalized healthcare.”
Cate Brinson, PhD | Sharon C. and Harold L. Yoh III, Distinguished professor
Through the graduate certificate in AI for Materials, you will learn to unlock the hidden potential of materials design as you acquire a vital toolkit forged through interdisciplinary study.
With the support of world-class faculty, your skills will be honed to further the boundaries pushed by AI integration in materials science.
MS, MEng and PhD students admitted to the certificate program will complete at least four data and material science courses–this doubles as an exciting opportunity to engage with computational methodology in the field.
Confront materials design problems
Certificate holders will be trained in both materials fundamentals and data science principles and will be empowered to tackle critical problems in the field's semi-infinite design space.
Build on your foundational knowledge
Supplement your materials science foundation with the necessary skills to evolve as a participant in the field.
Collaborate over cutting-edge research
Machine learning and materials science make it possible to be at the forefront of new material discoveries.
Three courses and a hands-on experience:
- 1 Materials Science Foundations Course
- 1 Machine Learning Foundations Course
- 1 Machine Learning and Materials Applications Course
- Select either ML + Materials Project or Internship
- Materials Science Foundations Courses
- ME 510: Diffraction and Spectroscopy, Delaire, Offered Spring
- ME 511: Computational Materials Science, Blum, Spring
- ME 512: Modern Materials, Payne, Fall
- ME 514: Theoretical and Applied Polymer Science, Zauscher, Fall
- ME 515: Electronic Materials, Curtarolo, Spring
- ME 519: Molecular Modeling of Soft Materials, Arya, Fall
- ME 555 Intermediate Polymer Physics, Rubinstein, offered occasionally
- ME 562: Materials Synthesis and Processing s, Mitzi, Fall
- ME 563: Fundamentals of Soft Matter, Rubinstein/ Zauscher, Spring
- ME 564: Introduction to Polymer Physics, Rubinstein, Fall
- ME 711: Nanotechnology Materials Lab, Walters, Fall, Spring
- CHEM 548 Solid-State/Materials Chemistry, Liu, offered occasionally
- CHEM 590 Polymer Synthesis, Becker, Spring
- CEE 520 Continuum Mechanics, CMSC faculty, Fall
- CEE 521 Elasticity, Brinson, offered occasionally
- ECE 511 Foundations of Nanoscale Science & Technology, Franklin, Fall
- ECE 524 Introduction to Solid-State Physics, Brown, Fall
- PHYS 516 Quantum Materials, Baranger, Fall
- Machine Learning Foundations Course
- ME 555-09/CEE 690 Data Science and machine learning for applied science and engineering, Carlson/Holt, Fall, Spring
- COMPSCI 671D:Theory and Algorithms for Machine Learning, C. Rudin, Fall
- ECE 685D: Introduction to Deep Learning, V. Tarokh, Once a year
- ECE 590: Advanced Deep Learning, V. Tarokh, Once a year
- ECE 590: Computer Engineering ML and Deep Neural Nets, Y. Chen/ H. Li, Once a year
- ECE 682D/STA 561D: Probabilistic Machine Learning, E. Laber, Once a year
- Machine Learning and Materials Applications Course
ME 582 Data and Materials Science Applications, Brinson/Arya/ Curtarolo/ Guilleminot/ Rudin/ Lu/ Jie Liu/ Banks, Spring
- ME 555: Sci Computing, Simulation and ML, Aquino, Spring
- ME 555: Applications of Computational Materials, Curtarolo, Spring
- ML + Materials Project or Internship
- Fall/Spring Data and Materials Science Capstone. 3 units
- Other research project courses or independent study course with work related to machine learning and materials science may satisfy this requirement, but require pre-approval from certificate administrators and faculty team review.
- Internship or external research experience related to machine learning and materials science. (approved as 3-4 unit course by DGS)
This certificate is open to all MS, MEng and PhD students at Duke University. Participating students may apply for the certificate anytime during their degree program as long as they meet deadlines of July 1 for Fall graduation and November 1 for Spring graduation. Certificates are awarded upon completion of the student’s degree program.
Ready to apply? Complete an online application.
To learn more about the AI for Materials Graduate Certificate, contact Shana McAlexander, aiM Associate Director at email@example.com