Machine Learning-based Materials Design
Required Availability
The End of TimeCourse Credit?
Yes - CHE 498Paid Position?
NoPreferred Majors
Chemical Engineering | Chemistry | Environmental Engineering | Materials Science | Mathematics | PhysicsFaculty
Tibor SzilvasiDescription
Designing new environmentally friendly and cheap materials for practical applications is one of the main challenges of our century. This process is however very slow because synthesizing and testing new materials take time and have considerable cost. Computational methods provide an alternative method to screen materials faster and circumvent the costly and slow experimental trial-and-error approach. In this research area, machine learning-based methods have emerged as flexible tools recently to predict the properties of hitherto unknown materials based on previously known information. The Szilvasi group is working on developing databases and machine learning-based workflows to design new materials in the area of catalysis, energy storage, and smart materials,
Special Directions
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