This project will explore deep neural networks and random forest regressors to predict system outputs of optimizing over 100 parameters and a chemical space of size ~10^10 to maximize production rate and process efficiency. In addition to the machine learning-based computational approach, the finite element computational simulations improve electrolyzer designing. This project builds upon a Mitacs project and will make use of data collected during XPRIZE operations. This project will contribute towards the optimization of CERT’s cell design for future operations.

Industry Partner(s):CERT Systems

Academic Institution:University of Toronto

Academic Researcher: Sargent, Ted

Focus Areas: AI

Platforms: GPU, Parallel CPU