Supervisors: Prof. Reinhard Maurer (Chemistry), Prof. Richard Beanland (Physics)
Understanding how local atomic structure and long-range emergent magnetic and electronic properties in defective 2D materials are connected is critical for the development of next generation functional materials. However, modern atomically-resolved imaging techniques only give an integrated snapshot of the structure, without revealing the details of the three-dimensional morphology or the stability: There are many ways to arrange atoms that give essentially the same 2D image.
The project will employ electronic structure calculations of well-characterized 2D materials, simulations of electron microscopy images, and machine learning methods to reconstruct the 3D atomic positions of materials from a 2D microscopy image. The student will work closely with experts at national spectroscopy and imaging facilities to deliver scientific software applicable to experimental imaging data.
Project Aims
The aim of this project is to create a computational tool based on experimental input, simulated data, and machine learning methodology to extract 3D atomic structure information from 2D identical location STEM images. STEM image data will be augmented with atomistic structure data from electronic structure theory and STEM image simulations. All data will be combined into an automated workflow that predicts thermodynamically stable nanoparticle structures that are most likely to give rise to the observed STEM images.
Project Outcomes
- Generation of a database of nanostructures from electronic structure theory calculations and transmission electron microscopy image simulations
- Development of a machine learning model capable of inferring 3D atomic structure from two-dimensional TEM projection images
- Application of the new approach to the characterization of metal nanostructures stabilised on defective graphene films
- Development of an automated 3D structure analysis software applicable for a broad range of scientific end users
Skills that the student will acquire
- Electronic structure theory calculations of materials, atomistic molecular simulation methods
- Experience with machine learning methods
- Expertise in surface science characterization techniques and multiple scattering simulations of transmission electron microscopy images.
- Software development in Python
Find out more: https://warwick.ac.uk/fac/sci/hetsys/themes/projects2025
About us:
The EPSRC Centre for Doctoral Training in Heterogeneous Modelling (HetSys), based at the University of Warwick, offers an exceptional opportunity for students from physical sciences, life sciences, mathematics, statistics and engineering backgrounds who are passionate about applying their mathematical expertise to tackle complex, real-world problems.
By fostering these skills, HetSys trains the next generation of experts to challenge the cutting-edge of computational modelling in diverse, heterogeneous systems. These systems span a wide range of exciting research areas, including nanoscale devices, innovative catalysts, superalloys, smart fluids, space plasmas, and more.
HetSys offers a vibrant and supportive research environment, ideal for nurturing creativity and academic growth. Our interdisciplinary student community spans multiple cohorts, each at different stages of their PhD journey, creating a rich, collaborative atmosphere.
Funding Details
Additional Funding Information
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.
For more details visit: https://warwick.ac.uk/fac/sci/hetsys/apply/funding/
Standard UKRI rate
