PhD studentship in Machine Learning for Computational Physics and Chemistry

PhD studentship in Machine Learning for Computational Physics and Chemistry, University College London, UK

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: Perturbed Neural Network Potential Molecular Dynamics.

The interaction of molecular and condensed phase systems with external electric fields is of major importance in a myriad processes in nature and technology, ranging from field-directed chemical catalysis to energy storage systems including supercapacitors and batteries.

The overall aim of this project is to further develop and apply machine learning methods, in particular our recently introduced perturbed neural network potential molecular dynamics (PNNP MD), to boost the time and length scales accessible to the atomistic simulation of such systems at quantum mechanical accuracy.

Several applications of this method are envisaged aiming to understand at an atomistic level how, e.g., electric fields modify ionic conductivity and chemical reactivity of electrolytes in next-generation energy storage systems or how electric fields affect ion adsorption and the charging of electrochemical interfaces.

Interested candidates may want to take a look at our recent work on PNNP MD: 

https://www.nature.com/articles/s41467-024-52491-3

Project 2: Non-adiabatic Molecular Dynamics.

Atomistic Simulation of electronically excited processes in molecules and materials is essential for our understanding of the working principles of important energy conversion technologies.

The overall aim of this project is to further develop our recently introduced non-adiabatic molecular dynamics simulation method, termed excitonic state-based surface hopping (X-SH) e.g. by developing machine learning methods for more accurate but still ultrafast prediction of electronic Hamiltonian matrix elements.

Several applications in collaboration with experimental groups are anticipated aiming to understand at atomistic resolution how electronic excitations ("excitons") dissociate to charge carriers in organic solar cell materials or how a temperature gradient is converted to electricity in thermoelectric devices.

Interested candidates may want to take a look at our recent work,

https://www.nature.com/articles/s41467-022-30308-5 &

https://www.science.org/doi/10.1126/sciadv.adr1758

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 covers university fees and maintenance.

Candidates from the UK, EU and the rest of the world are welcome to apply.

Please submit applications in the following format:

• A personal statement (500 words maximum) outlining:
  • (i) your research experience,
  • (ii) which project you would like to work on and your suitability for the project,
  • (iii) what you hope to achieve in your PhD.
• A CV including full details of all University course grades, details on any 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 March 2025.

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