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Machine-learning design of novel magnets


Project Description

A PhD position is available from September 2019 in the School of Physics and the CRANN Institute (www.crann.tcd.ie) at Trinity College Dublin (Ireland). Sponsored by Science Foundation of Ireland (SFI) and the Irish Research Council (IRC) this is part of a large effort for developing and implementing machine-learning methods for materials modeling. The project will be hosted by the Computational Spintronics Group (www.spincomp.com), headed by Prof. Sanvito, and is strongly connected with the experimental activity at CRANN and the AMBER research center (ambercentre.ie). The project will include methodological algorithm development and materials science.

Project Description
The PhD position will be part of a large project aiming at the computational design of novel magnets for a range of applications (electric motors, data storage, sensing, antennas, etc.). We will use machine-learning methods trained over large experimental and theoretical datasets to explore a vast chemical and structural space. These will provide a first pool of materials prototypes, whose electronic and magnetic properties will be calculated with advanced electronic structure theory (density functional theory) operated in a automatized high-thoroughput mode. Then, for the most promising materials, we will construct state-of-the-art machine learning force fields and with these explore their finite-temperature behaviour. The project will maintain a close collaboration with experimental groups at Trinity, who will attempt the synthesis of the most promising magnets identified by the theory. Part of the research will be conducted in collaboration with Prof. Curtarolo’s Materials Lab at Duke University. The project will cover the following topics:
1a) Construction of machine-learning models based on theory and experimental data for magnetism.
1b) High-throughput magnetic materials design with advanced electronic structure theory.
1c) Development of machine-learning force fields for spin dynamics.

Essential/Desirable Criteria
Strong overall motivation and a keen interest in theory and computation, as well as in interdisciplinary work between physics and materials science. Previous experience in UNIX/Linux environment and with programming in either Fortran and/or C/C++. Ability to work independently and also function as an active and efficient team player. Good writing skills. Previous knowledge of density functional theory and/or electronic structure methods will be considered as an advantage.

Funding Notes

The position is fully funded (fees plus stipend) for a duration of 4 years (the time needed to complete the degree).

Applications must include a cover letter detailing how you meet the selection criteria for the post, together with a CV and the name and contact details of at least two referees (e-mail address). Informal inquiring and applications should be sent to:
Prof. S. Sanvito (). Information on the group activity can be found at View Website

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