BYU

Abstract by Chandramouli Nyshadham

Personal Infomation


Presenter's Name

Chandramouli Nyshadham

Degree Level

Doctorate

Co-Authors

Kennedy Lincoln
Gus Hart

Abstract Infomation


Department

Physics and Astronomy

Faculty Advisor

Gus Hart

Title

Insights on materials space

Abstract

In recent years, surrogate machine-learning models have transformed computational materials science by predicting properties of materials with the accuracy of quantum mechanics at a fraction of the cost. Using a kernel-based machine learning surrogate model, we present few insights on generating and choosing the training data for optimal modeling of materials space.  Our insights are based on analyzing data from over 73,000 unrelaxed DFT calculations comprising 45 different materials and helped improve our model’s predictions by as much as 30% for some materials.