Material discovery and modelling for solid-state hydrogen storage and fuel cell applications

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Material discovery and modelling for solid-state hydrogen storage and fuel cell applications

This thesis covers an attempt to construct a supervised machine learning model, for use in prediction of formation enthalpy values for novel metal hydride compositions. Further work, making use of static density functional theory calculations as well as ab initio and machine learning force field mol...

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Bibliographic Details
Main Author:	Wakerley, James Kenneth Malcolm
Format:	Thesis (University of Nottingham only)
Language:	English
Published:	2023
Subjects:	Hydrogen as fuel; Fuel cells; Machine learning; Heat of formation; Enthalpy; Hydrides; Titanates
Online Access:	https://eprints.nottingham.ac.uk/73082/

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