BYU

Abstract by Kennedy Lincoln

Personal Infomation


Presenter's Name

Kennedy Lincoln

Co-Presenters

None

Degree Level

Undergraduate

Co-Authors

None

Abstract Infomation


Department

Physics and Astronomy

Faculty Advisor

Gus Hart

Title

Representations in Machine Learning

Abstract

Currently, computationally finding new materials with high accuracy is expensive and time-consuming. Machine learning can help us reduce the cost of calculations to a great extent.  For machine learning methods to be effective, we need a unique representation of crystal structures. Describing the atomic systems require a representation that is invariant to translations, rotations, and permutations of the crystal structure. In this talk, we will present the importance of representations in machine learning with a simple example.