Machine Learning for the Discovery of Metal-Organic Frameworks for Hydrogen Storage Applications
Lead Research Organisation:
Cranfield University
Department Name: School of Water, Energy and Environment
Abstract
EPSRC : Paula Nkulikiyinka : EP/R513027/1
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 - a form of machine learning) 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.
This research will be conducted by a PhD student (Paula Nkulikiyinka, who is funded by an EPSRC DTP grant) in collaboration with Dr A Howarth's research group at Concordia University, Canada. The research proposal has been discussed and agreed by both Dr Clough and Dr Howarth.
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 - a form of machine learning) 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.
This research will be conducted by a PhD student (Paula Nkulikiyinka, who is funded by an EPSRC DTP grant) in collaboration with Dr A Howarth's research group at Concordia University, Canada. The research proposal has been discussed and agreed by both Dr Clough and Dr Howarth.
