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The Montemore Research Group

Computational Design of Materials for Energy

 

Research Interests

Our focus is developing and applying efficient methods for designing materials for energy applications. These materials include surfaces, nanoparticles, 2-D materials, interfaces, bulk materials, and liquids. Possible applications include catalysts, solar cells, batteries, and nanoscale devices.

 

We apply a variety of computational and theoretical tools, including density functional theory, thermodynamic and kinetic modelling, machine learning, and semi-empirical modelling.

Efficient Design of Catalysts

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Machine Learning and Data Science for Materials Design

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Excited State Dynamics

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Research

We have a variety of research interests relating to computational materials design.

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Team

Our Team On The Forefront Of Research

Software and Resources

 

SurfEP

A Python program that allows predictions of adsorption energies of a variety of atoms and small molecules on many transition metal alloy surfaces.

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XPS Fitting From Density of States

A Python script that is useful for fitting peaks from XPS.

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ExCiteSearch

A Python program that locates relevant literature, based on papers that are already known to be relevant.

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