PhD Studentship: Machine Learning Multiscale Simulation of Photoconductivity in Correlated Oxides

at University of Warwick
Published December 14, 2022
Location Coventry, United Kingdom
Category Machine Learning  
Job Type Scholarship  



Nicholas Hine (Physics), Reinhard Maurer (ChemistryPhysics), Marin Alexe (Physics)


Predicting, explaining and modelling novel behaviours of quantum materials requires a combination of theoretical insight with state-of-the-art multiscale modelling. In the case of complex oxides, displaying both strong electronic correlation and a diverse range of extended and point defects, traditional electronic structure methods encounter severe challenges when trying to model key properties such as photoconductivity and bulk photovoltaic effects. Fortunately, the extraordinary speed and power of machine-learned interatomic potentials provides a brand-new way to gain insight into these systems. This project will design and build multiscale models to understand photoconductivity in SrTiO3, particularly enhancement associated with dislocation cores.


The transport properties of traditional semiconductors can be understood through well-established theoretical methods. Dislocations, grain boundaries and point defects typically reduce conductivity in such materials. The situation can be very different for complex oxide materials such as SrTiO3. In these systems, defects are often what leads to desired functionality in the first place. Recently, highly unexpected behaviour of photoconductivity has been observed at dislocations in SrTiO3 [1]: different slip systems display different photoconductivity, by several orders of magnitude, and a photovoltaic effect enabled by dislocation-surrounding strain fields was observed, demonstrating great scope for tailored, anisotropic photoelectric functionality.

The main barrier to understanding is that complex oxides exhibit strong electron correlation, localized electrons, and the formation of polarons, which provide a challenge for existing electronic structure theory and conventional transport theories. In such materials charge carriers are strongly correlated with lattice vibrations, and thus cannot be described with traditional band theory [2]. Additionally, the intrinsic scale of dislocation defects, spanning hundreds to thousands of atoms, precludes the outright application of expensive correlated electronic structure methods.

This project addresses this challenge by establishing a multiscale simulation framework to study conductivity in nanostructured correlated oxides. Machine learning methods will provide capabilities to sample the structure and lattice dynamics of defects at realistic scales, whereas Hilbert space projection techniques [3] will provide paths to map correlated electronic structure onto viable models to simulate polaron formation and transport.

[1] Kissel et al Adv. Mater. 34 2 (2022)

[2] Zhou and Bernardi, Phys Rev Research 1 033138 (2019)

[3] Westerhout et al 2D Mater 9 014004 (2022).

For further details about the project and how it links to the training included in the HetSys PhD programme, please visit:

Machine learning multiscale simulation of photoconductivity in correlated oxides (


Awards for both UK residents and international applicants pay a stipend to cover maintenance as well as paying the university fees and a research training support. The stipend is at the standard UKRI rate.

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