Machine Learning for the Discovery of Metal–Organic Frameworks for Hydrogen Storage Applications

Metal–organic frameworks (MOFs) are a class of crystalline materials with ultrahigh porosity, and high surface areas. With these properties, along with variability for both the organic and inorganic components of their structures, MOFs are of interest for potential applications in clean energy, most significantly as storage media for gases such as hydrogen and methane, and as high-capacity adsorbents to meet various separation needs. The use of quantitative structure-property/ activity relationships (QSPRs) is an emerging and helpful mathematical tool that allows the link between physical or chemical properties to predict the behaviour or desired characteristic of a molecule. The purpose of this study is to exploit both fields in order to obtain the optimal MOF for hydrogen storage, as well as investigating what parameters affect the hydrogen uptake capabilities. Following the screening of optimal MOFs, they will be synthesized in the lab to test performance and validate the methodology.

Faculty Supervisor:

Ashlee Howarth

Student:

Partner:

Cranfield University

Discipline:

Engineering

Sector:

Clean Technology; Green/Alternative Energy; Energy and Utilities

University:

Concordia University

Program: