Green Chemistry & Materials

Modelling (GCMM)
Laboratory

Sustainable synthesis of functional crystalline
materials powered by computational insight

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Dr. Sharmarke Mohamed

Dr. Sharmarke Mohamed (MSci, PhD, MRSC) is an Associate Professor of Chemistry and PI of the Green Chemistry and Materials Modelling Laboratory (GCMM). Dr. Sharmarke has extensive research, teaching, mentoring, supervision and leadership experience within the higher education sector. He has interdisciplinary research interests covering green chemistry, computational chemistry and chemical crystallography. Since 2014, he has been serving on the faculty at Khalifa University (KU).

Our Research

We apply computational materials modelling methods to predict, design and realize new solid-state structures of organic molecules. Crystal structure prediction, DFT and molecular mechanics methods are all areas of particular interest for our group.

Our work in this area focuses on the development of mechanochemical methods for the synthesis and functionalization of new organic molecules, complexes and materials with useful properties.

We work with chemists and engineers to support the discovery of functional 2D and 3D materials with desirable mechanical and optoelectronic materials. We do this by using a range of custom python workflows we developed using data generated from VASP or CASTEP.

Research Focus

Our laboratory, which is based in the Chemistry Department of KU brings together a diverse group of synthetic organic chemists, crystallographers, materials scientists and computational chemists. We are interested in the development of green mechanochemical methods for the development of new optoelectronic, area.

Latest News

Sustainable synthesis of functional crystalline materials powered computational insight

Sustainable synthesis of functional crystalline materials powered computational insight

Sustainable synthesis of functional crystalline materials powered computational insight

Sustainable synthesis of functional crystalline materials powered computational insight