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PhD position in Machine-Learning Force-Fields fo ... (No replies)
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Offer description:
This project will develop a machine-learning software to simulate spectroscopic properties of interfaces with neural-network force fields using analytical second-derivatives. This will allow for a numerically precise method (much more than a frozen-phonon approach) capable of delivering the vibrational frequencies of systems with thousands of atoms with DFT quality. This tool will then be used to investigate the localized vibrational modes of several interfaces. We will then extend our software to provide the analytical third-order derivatives that open the door for the investigation of thermal transport properties and of Raman spectra. The energetically favoured interface reconstructions will be efficiently identified by crystal structure prediction, using machine learning force fields, together with a constrained variant of the minima hopping method. Electronic band diagrams across the interface will also be calculated using density functionals for interfaces. The PhD student will integrate the group of Miguel Marques at the Institute of Physics of the Martin-Luther University of Halle-Wittenberg in Halle (Saale), Germany.
Skills/Qualifications:
We are searching for a candidate with a master degree in Physics, Chemistry, or Computer Science, a sound knowledge of solid-state physics and/or quantum chemistry, and good programming knowledge (at least in python). Knowledge of machine-learning theory and programming frameworks (in particular pytorch) is appreciated (but not required).
Eligibility criteria:
EU mobility rules apply. In principle, applicants can have any nationality and any current residence (although immigration rules apply, favoring EU applicants). Candidates who have already been awarded a PhD degree are not eligible. In addition, candidates who have already spent more than 12 months in the Germany within the last 3 years are not eligible (unless as part of a procedure for obtaining refugee status under the Geneva Convention).
How to apply:
Send your CV (including your grades in the bachelor and master) and a copy of your master thesis to Miguel Marques ([email protected]).