Machine-Learning for design and discovery of next generation CO2 electrocatalysts

Mitigation of CO2 emissions in conjunction with the implementation of renewable energy generation and storage are widely recognized among the most pressing technological challenges of the twenty-first century that aim to address runaway climate scenarios. The UBC team in collaboration with its industry partner (AGORA Energy Inc) has introduced the concept of CO2-to-energy via its unparalleled and proprietary CO2 Redox Flow Battery (CRB) technology. There is an ongoing collaboration among UBC and IEK-13 (Julich) to accelerate the commercialization and large-scale deployment of the CRB. The visiting student will participate to ongoing data-driven tasks related to materials discovery for new electrocatalysts based on Metal Organic Framework (MOF) where Artificial Intelligence (AI) models based on data analytics and machine learning are utilized. The collected data and ML-based modelings will be complemented by high-throughput electrochemical characterization methods for rapid screening of advanced catalysts, enhancing system design and optimizing operating conditions at UBC and AGORA.

Faculty Supervisor:

Elod Lajos Gyenge

Student:

Partner:

Forschungszentrum Jülich

Discipline:

Engineering

Sector:

Green/Alternative Energy; Artificial Intelligence; Information and Communications Technology

University:

The University of British Columbia

Program: