In the Arhangelskis group we develop methods for the computational design of crystalline materials with the aim of improving the speed and reducing the costs of materials development, while also driving our understanding of structure-property relationships.
Our major research interest is in the development of crystal structure prediction (CSP) of metal-organic frameworks (MOFs), which are functional materials constructed from transition metal nodes connected by organic linkers. MOFs are renowned for the diversity of functional applications, including gas storage, catalysis, materials for energy storage, magnetic materials and many more. The diversity of functional properties arises from the vast number of possible combinations of nodes and linkers, each leading to materials with different crystal structures and, consequently, functional properties. Design of new MOFs, therefore, requires extensive experimental screening of node and linker combinations, in a quest to find materials, showing desired functional properties.
The CSP method for MOFs, which we develop in collaboration with the Morris group from the University of Birmingham,1 provides an alternative approach to MOF design from a computational perspective: starting from a 2D diagram of nodes and linkers we are able to predict which crystal structures are most likely to form from experimentally, and then simulate the functional properties of the predicted structures. In particular, we have recently applied this method to the design of MOFs with potential application as rocket fuels.2
The current PhD project is dedicated to the application of CSP to the design of magnetic MOF materials. In these materials, the transition metal atoms with unpaired electrons carry magnetic moment, however, it is the organic linkers that allow to control the distance between magnetic centers and their orientation, thereby influencing the overall magnetic performance.3 Our objective is to reliably predict structures of MOFs that possess strong magnetic performance at possibly higher temperatures.
The successful candidate will use state-of-the art computational methods, namely periodic density-functional theory (DFT) calculations and machine learning (ML) to predict the crystal structures and magnetic properties (magnetic ordering, Curie temperature) of putative MOF structures.
The studentship also includes a 6-month internship (supported by NAWA) with Dr. Krunoslav Užarević at the Ruđer Bošković Institute (Zagreb, Croatia), where the candidate will receive training in the experimental synthesis and characterization of MOF materials, including magnet measurements.
To enquire about the project please email [Email Address Removed]. For further information about the Arhangelskis group please visit the group website www.arhangelskis.org
Applications should be made via the following link: https://irk.uw.edu.pl/en-gb/offer/SzD2023/programme/3-SzD-NSP-NChem-PreludiumBis-MA/?from=field%3ADS010604N
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