PhD Studentship in Computational Materials Chemistry and Machine Learning

University of Birmingham - School of Chemistry

Background: Functional, topologically complex organic molecules are rising stars in modern materials science due to their biocompatibility, structural variability, and wealth of physico-chemical properties. Our group uses theoretical and computational chemistry, physics, and materials science in combination with chemical machine learning to explore and exploit diverse functional organic and hybrid materials and molecules. We are particularly interested in graphene-based materials, covalent-organic frameworks and cages, and hyperbranched polymers in the context of their applications in capture, storage, transport, and/or catalytic transformations of therapeutic molecules and environmental pollutants.

Project: This position allows adaptive approach to the central topic of the PhD depending on your background and interests. Possible research directions include constructing multiscale simulation workflows and applying them to materials design, and/or developing and implementing novel machine learning representations for functional organic materials. You will work in close cooperation with other group members and receive an in-depth training in a range of simulation techniques and in various aspects of chemical machine learning. This position will equip you with a competitive professional profile and a range of transferrable skills for both academia and industry.

The project will be supervised by Professor Ganna (Anya) Gryn'ova (g.grynova@bham.ac.uk).

Funding notes:

This studentship is fully funded for 3.5 years and includes a tax-free annual stipend and fees at the UK home rate. Additional funding will be available to cover research and training costs, conference attendance, etc. Due to funding restrictions, applicants not eligible for UK home fee status will only be considered if they can secure additional external funding to cover international fees.

References:

1. R. Fedorov, A. Nihei, G. Gryn’ova, Multi-Solvent Graph Neural Network for Reduction Potential Prediction across the Chemical Space, J. Chem. Inf. Model. 2026, 66, 847-854.
2. Ernst, R. Fedorov, A. Calzolari, C. Mollart, F. F. Grieser, S. Ber, G. Gryn'ova, Fragment to Framework: Automatic Fragmentation of Covalent Organic Frameworks into Building Blocks for Band Gap Analysis, Mater. Chem. Front. 2026, 10, 617-623.
3. Ehlert, A. Piras, G. Gryn’ova, CO2 on Graphene: Benchmarking Computational Approaches to Non-Covalent Interactions, ACS Omega 2023, 8, 35768.
4. Llenga, G. Gryn’ova, Matrix of Orthogonalized Atomic Orbital Coefficients Representation for Radicals and Ions, J. Chem. Phys. 2023, 158, 214116.