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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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development with broad, long-term impact. Not only will you gain insights into fundamental atomistic properties of alkali metals, but you’ll also contribute to pioneering computational tools that extend far
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will be augmented with atomistic structure data from electronic structure theory and STEM image simulations. All data will be combined into an automated workflow that predicts thermodynamically stable
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model system: it is an inherently defective system (like almost all real catalysts) that can be anchored to supports amenable to fundamental atomistic studies. This research will build upon an ongoing