PhD Studentship in Computational Chemistry - Machine Learning Potentials for Molecular Materials

Overview

Interested in machine learning (ML) for molecular modelling? This PhD project will develop machine learning inter-atomic potentials (MLIPs) for molecular materials and organic-inorganic interfaces relevant to energy storage technology and medicinal applications. 

The field of ML is rapidly evolving, and it is poised to become central to the future of atomistic simulations. The efficiency and accuracy of MLIPs have been thoroughly studied on fixed train/test datasets but the performance of these models in actual molecular dynamics (MD) remains poorly understood. Condensed phase molecular systems are particularly challenging to model owing to a large difference in scale between intra- and inter-molecular interactions. 

This studentship will address the following questions: 

1) How can we enhance ML techniques to improve the accuracy of inter-molecular interactions in the condensed phase? 

2) How can we adapt current active learning protocols to speed up the development of MLIPs in soft molecular systems? 

3) Can we use accurate inter-molecular MLIPs to better understand ion transport in rechargeable batteries? How about drug binding in proteins for medicinal applications? 

Through the completion of this PhD, you will develop your scientific expertise in molecular modelling for applications in renewable energy materials, and will improve your skills in computer simulations, ML and data science, in general. We will work in collaboration with Professor Gábor Csányi (University of Cambridge), Dr James Dawson (NUAcT at Newcastle University) and Dr Daniel Cole (UKRI FLF at Newcastle University). 

Number Of Awards

1

Start Date

18th September 2023

Award Duration

3 Years

Sponsor

EPSRC 

Supervisors

Dr. Ioan-Bogdan Magdău 

Eligibility Criteria

You must have, or expect to gain, a minimum 2:1 Honours degree or international equivalent in computational Chemistry related to the proposed PhD. 

Home and international applicants (inc. EU) are welcome to apply and if successful will receive a full studentship. Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in all sub-skills.

International applicants may require an ATAS (Academic Technology Approval Scheme) clearance certificate prior to obtaining their visa and to study on this programme. 

How To Apply

You must apply through the University’s Apply to Newcastle Portal 

Once registered select ‘Create a Postgraduate Application’. 

Use ‘Course Search’ to identify your programme of study: 

• search for the ‘Course Title’ using the programme code: 8100F 
• Select ‘PhD Chemistry (full time) - Chemistry’ as the programme of study 

You will then need to provide the following information in the ‘Further Questions’ section: 

• a ‘Personal Statement’ (this is a mandatory field) - upload a document or write a statement directly in to the application form 
• the studentship code SNES234 in the ‘Studentship/Partnership Reference’ field 
• when prompted for how you are providing your research proposal - select ‘Write Proposal’. You should then type in the title of the research project from this advert. You do not need to upload a research proposal. 

Contact Details

Dr. Ioan-Bogdan Magdău 

Lecturer in Computational Chemistry 

School of Natural and Environmental Sciences 

Email: i.b.magdau@gmail.com