Why Apply - Is aiM right for me?

Scientific machine learning provides a new pathway to accelerate understanding and discover new materials that address global challenges across many sectors including the energy sector (e.g., battery technology), transportation (e.g., lighter weight aerospace materials), and health care (e.g., drug delivery devices, soft-tissue imaging, and implant materials) and many more areas. 

"I joined aiM to improve my understanding and how I can implement AI/ML in the materials science domain.”

- Nicholas Finn, aiM Trainee

"I joined aiM for its rich learning opportunities and collaborative environment.” 

- Peiyi Chen, aiM Trainee 

For Materials Science, Engineering, Chemistry, Physics Majors

In light of the Materials Genome Initiative, data science, and AI are being pursued to tackle problems in materials science across all materials domains, and are a key part of both developing a new understanding of underlying materials physics as well as developing robust methods for design of new materials. A few examples of open problems include:

  1. How to develop high-dimensional generative models for material behaviors.
  2. How to explore and use the underlying structure in data to develop invertible reduced-order models, suitable for computational analysis.
  3. How to leverage AI to improve efficiency and sustainability of material design by predicting the best next experimental sample/test conditions.
  4. How to use AI to bridge data and models across length and time scales to leverage quantum or atomistic features in predictions/understanding of macro-scale material response. Solutions to this type of scaling issue could have implications across all science.


AI algorithms and machine learning strategies are in high demand to help overcome many scientific and engineering challenges. The field of materials science is rich in image-based datasets (e.g., many forms of micrographs of material structure), model-based datasets (e.g., physics-based predictive modeling codes at all length scales), and wide-ranging feature sets (e.g., processing parameters, environmental conditions, test parameters). Scientific machine learning can be applied to:

  1. Creating an interpretable neural network that tells you what patterns it's using to classify materials.
  2. Designing efficient optimization techniques to search through a large combinatorial space of materials.
  3. Building physics into AI algorithms in order to better model a materials system.