Machine learning as a tool for interpreting variables in hydrogen sorption data

Muhammad Irfan Maulana Kusdhany, Stephen Matthew Lyth

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

To realize the hydrogen economy, it is important to develop methods of storing hydrogen that are cost-effective, safe, and compact. One method of storing hydrogen which shows a lot of potential is through physisorption on high surface area carbon. However, the scientific literature offers conflicting information on which properties of the carbons are important to hydrogen storage and to what extent. This is chiefly because each experimental study on carbon materials only analyzes results on an incredibly small subset of carbon materials. To remedy this, we conducted an integrative data analysis wherein we collected experimental data from many studies in the literature to construct a large dataset. We then used this dataset to develop a machine learning model which can predict the hydrogen adsorption isotherm at 77K based on the porosity and chemical composition of the carbon. By analyzing this model using a post-hoc explanation method called Shapley Additive Explanations, we can analyze the structure-property relationships clearly.

Original languageEnglish
Title of host publicationProceedings of WHEC 2022 - 23rd World Hydrogen Energy Conference
Subtitle of host publicationBridging Continents by H2
EditorsIbrahim Dincer, Can Ozgur Colpan, Mehmet Akif Ezan
Place of Publication[S.I.]
Pages590-592
Number of pages3
ISBN (Electronic)9786250008430
Publication statusPublished - 30 Jun 2022
Event23rd World Hydrogen Energy Conference: Bridging Continents by H2, WHEC 2022 - Istanbul, Turkey
Duration: 26 Jun 202230 Jun 2022

Conference

Conference23rd World Hydrogen Energy Conference: Bridging Continents by H2, WHEC 2022
Country/TerritoryTurkey
CityIstanbul
Period26/06/2230/06/22

Keywords

  • hydrogen storage
  • machine learning
  • porous carbon

Fingerprint

Dive into the research topics of 'Machine learning as a tool for interpreting variables in hydrogen sorption data'. Together they form a unique fingerprint.

Cite this