FURI | Fall 2018
Machine Learning Models for Accelerating Discovery and Design of Energy-Related Materials
Machine learning (ML) models have become essential to the growth the of materials science and engineering. Effective utilization of ML could accelerate the discovery of new materials through cooperative applications of data, algorithms, and fundamentals of materials theory. Hence, the research team is focusing on applying ML models to accelerate discovery and design of energy-related materials, such as Pt-efficient catalysts for hydrogen evolution and structural engineering alloys, through identification of descriptors representing the best correlation among complex, high-dimensional data of materials properties.
Graduation date: Spring 2019