Materials informatics: Moving beyond screening via generative machine learning models
Abstract: Technology progresses only as fast as the development of new, advanced materials. Materials discovery has never been more important, but it is far too slow and expensive. Materials informatics has accelerated materials development, but primarily allows us to screen known materials as opposed to truly discover new materials. Here, I will describe our efforts to generate new periodic crystalline materials by predicting crystallographic information file data using generative adversarial networks in conjunction with the newly published DiSCoVeR algorithm that combines a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model.
Bio: Dr. Sparks is an Associate Professor of Materials Science and Engineering at the University of Utah. He holds a BS in MSE from the UofU, MS in Materials from UCSB, and PhD in Applied Physics from Harvard University. He was a recipient of the NSF CAREER Award and a speaker for TEDxSaltLakeCity. When he’s not in the lab you can find him running his podcast “Materialism” or canyoneering with his 4 kids in southern Utah.