Project Info

Informatics Analysis of an Experimental Materials Database

Eve Mozur
Hua Wang

Project Goals and Description:

Historically, making materials (from stainless steel to battery cathodes) has been an empirical process driven by trial and error. Attempts to optimize synthesis and processing can therefore be incredibly time intensive. The advent of new computational methods capable of recognizing patterns hidden to the human perspective is changing how materials scientists think about this classic problem. In this project, we will use an existing database of synthetic parameters mined from the literature to search for multi-dimensional patterns in the preparation of manganese intermetallic materials. These types of materials are important for a variety of applications, including metallurgy, magnetism, and transport.
As the project needs insight from both disciplines, the student will meet with graduate students or professors from each field one-on-one regularly. This will be particularly important as the student learns the relevant methods from each field. Everyone on the project will meet monthly or bi-monthly as appropriate to brainstorm, problem solve, and assess progress.

More Information:

Grand Challenge: Engineer the tools of scientific discovery.

Primary Contacts:

Eve Mozur, | Hua Wang,

Student Preparation


The applicant student is expected to have already taken CHGN 121 and 122 or equivalent course and CSCI 261, 262, or 280 before taking this project. It would be beneficial if the student has already taken CSCI 303, 358, 404,470, but this is not required.




The student will learn methods in machine learning, as well as gain knowledge in material preparation.


The student will be considered a member of the Mozur Research Group in MME and the MInDS@MINES lab led by Prof. Wang. They will be invited to regular group meetings to gain perspective in each field as well as exposure to methodology. The student will meet bi-weekly with Prof. Mozur to discuss the materials science side of the project.

Preferred Student Status

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