Atomic-Level Defect Characterisation via Data Fusion: Integrating Ptychography, EELS and First-Principles Calculations
This project aims to enhance the understanding of 3D atomic structures and defects in materials using advanced imaging and computational techniques. We will integrate super-resolution computational imaging (ptychography), chemical spectroscopy (EELS), and density functional theory (DFT) to achieve high-resolution reconstructions of atomic configurations. By fusing data from these methods, we will improve both lateral and depth resolution while reducing electron induced sample damage. Machine learning will be employed to refine our models iteratively, leading to precise predictions of material properties, particularly around defects. This research will provide valuable insights into the physical characteristics that impact material performance.
Supervisors
Primary: Dr Peng Wang, Physics
Prof. Nicholas Hine, Physics
Summary
Defect characterization is essential because defects within crystals significantly influence their electrical, mechanical, thermal, and optical properties. Traditional methods for studying atomic structures are often time-consuming, potentially damaging, and limited in resolution. This research project leverages advanced computational material modelling to predict atomic-level structural configurations around defects in advanced materials. We will develop a data fusion approach that combines ptychography, EELS, and DFT to accelerate atomic structure analysis. Our goal is to reconstruct 3D atomic coordinates with sub-angstrom resolution and picometer precision, providing valuable insights into the material's physical properties, particularly around defects.
Background
Studying defects in materials is essential because these imperfections in atomic or molecular structures significantly impact a material's performance, efficiency, and stability. Novel materials often exhibit complex atomic structures with heterogeneous compositions, making it challenging to directly image defects at atomic resolution in three dimensions (3D). Traditional 3D imaging techniques, like tilt-series imaging and atom probe tomography, are time-consuming, require high electron doses, and can cause beam damage to samples.
Ptychography, an advanced computational imaging technique, offers an alternative with super-resolution and low-dose capabilities but is limited in depth resolution. Electron energy loss spectroscopy (EELS) complements ptychography by providing atomic-scale chemical information. When combined with density functional theory (DFT) for predictive modelling, a fully understanding of atomic configurations around defects becomes possible. This project will integrate ptychography, EELS, and DFT to create high-resolution 3D atomic reconstructions, further refined with machine learning to achieve sub-atomic resolution defect characterisation without beam damage.
Project Objectives for the PhD Project
This project aims to develop a robust, data fusion-based methodology for precise 3D atomic defect characterization in advanced materials by combining ptychography, EELS and DFT within a machine learning framework. This approach will enable in-depth analysis of defect structures without sample damage, advancing our understanding of defect-related properties.
Outcomes
1. Developed a new, data fusion methodology that combines ptychography, EELS, and DFT, allowing for high-resolution 3D reconstruction of atomic defects.
2. Improved understanding of the effects of atomic-level defects on the physical properties of materials, providing valuable information for material optimization.
3. Reduced electron dose requirements and minimized sample damage, making high-precision analysis more accessible for sensitive materials.
References
[1] Mao, W., Zhou, L., Wang*, P. Electron Ptychography, in Encyclopedia of Condensed Matter Physics (Second Edition) (ed Tapash Chakraborty) 71-94 (Academic Press, 2024).
[2] Hage, F. S., Radtke, G., Ramasse, Q. M. Single-atom vibrational spectroscopy in the scanning transmission electron microscope. Science 367, 1124-1127, (2020).
[3] Goode, A. E., Hine, N. D. M., McComb, D. W. Mapping functional groups on oxidised multi-walled carbon nanotubes at the nanometre scale. Chem. Commun. 50, 6744-6747, (2014).
[4] Radamson, H.H. Electron Microscopy. In: Analytical Methods and Instruments for Micro- and Nanomaterials. (2023).
How to apply
This is a fully-funded 4-year PhD position based in the HetSys Centre for Doctoral Training at the University of Warwick. All applications must be made through the University's postgraduate application form with a deadline of 20 January 2025. Please see our How to ApplyLink opens in a new window page for further details on the application process. For further information about student funding, the integrated HetSys training programme and what life is like in the HetSys CDT, please visit the Study with Us page.