PhD in Machine Learning for Computational Physics & Chemistry

A 3.5-year PhD studentship is available to work under the supervision of Prof Jochen Blumberger at the Condensed Matter and Materials Physics Laboratory, University College London, UK. Interested candidates may want to work on one of the following two projects.

Project 1: The overall aim of this project is to contribute to a step change in our fundamental understanding of electronically excited processes in molecular materials that are of direct relevance to energy conversion technologies. This will be achieved through the development and application of novel computational methodologies that are more accurate, predictive and applicable to significantly larger systems than existing approaches. The project will contribute to this endeavour by developing machine learning methods for ultrafast calculation of electronic Hamiltonian matrix elements and their implementation in our in-house non-adiabatic molecular dynamics software X-SH. Your implementation will then be used to simulate the dissociation of electronic excitations (excitons) to charge carrier in organic solar cell materials or the conversion of a temperature gradient to electricity in organic thermoelectric materials or to control the motion of charge carriers in organic semiconductors by external electric fields. The applications will be carried out in collaboration with world-leading experimental groups with the aim to explain experimental results on an atomistic level of detail and to obtain structure-function relationships for materials design. Interested candidates may want to take a look at our recent work on non-adiabatic molecular dynamics simulations, https://www.nature.com/articles/s41467-022-30308-5 andhttps://www.science.org/doi/10.1126/sciadv.adr1758

Project 2: The overall aim of this project is to develop and apply machine learning methods that enable a major boost of the time and length scales accessible to molecular dynamics simulations at ab-initio accuracy. Specifically, you will further develop our recently introduced perturbed neural network potential (PNNP) approach to leverage machine learning MD simulation of condensed phase systems in their electronic ground state interacting with external electric fields. Using this methodology you will investigate how electric fields modify chemical reactivity and ion adsorption at solid/liquid interfaces at atomistic resolution. Surface sensitive vibrational spectra will be calculated to validate the predicted atomistic structures and reactivities against experimental data. The detailed molecular understanding of electric field effects at solid/liquid interfaces obtained in this work will underpin the rational design of improved electrodes and electrolytes for diverse energy conversion applications. Interested candidates may want to take a look at our recent work on perturbed neural network potential simulations:  https://www.nature.com/articles/s41467-024-52491-3

Access to high performance computing facilities including CPU and GPU clusters will be provided throughout either project. 

Highly motivated students from Physics, Chemistry or Materials Science Departments are strongly encouraged to apply for this post. The candidate should have, or be about to receive, an honours degree (at least II.1 or equivalent) in Physics, Chemistry or a related subject. Good knowledge in quantum mechanics and statistical mechanics is expected. Some experience with molecular simulation and scripting languages (e.g. python) is a plus. 

The start date of the studentship is 22. September 2025. The studentship will cover all university fees and includes funds for maintenance at the standard UK rate and for participation in conferences and workshops. Due to funding restrictions, this studentship is open only to candidates from the UK or from the EU with pre-settled status in the UK. Please refer to the following website for eligibility criteria: https://www.ucl.ac.uk/prospective-students/graduate/research-degrees/physics-and-astronomy-mphil-phd

Please submit applications in the following format:

* A personal statement (500 words maximum) outlining (i) your research experience to date, (ii) which project you would like to work on (project 1 or 2) and your suitability for the project, with reference to the criteria in the above person specification, (iii) what you hope to achieve in your PhD.

* A CV, including full details of all University course grades to date, and, if relevant, details on scholarships, prizes and scientific papers published or in preparation.

* Academic transcripts for undergraduate (Bachelor) and graduate (Master) studies.• Names, and email addresses of two academic or professional referees (at least one academic).

These four documents should be submitted as a single zip file to Jochen Blumberger, j.blumberger@ucl.ac.uk specifying in the subject line “PhD application”. The closing date for applications is 28. February 2025. Applications received after this date may be considered only if a suitable candidate has not been found by the above closing date.   

Informal enquiries regarding the vacancy can be made to Prof Jochen Blumberger, j.blumberger@ucl.ac.uk.