Jasper Allen

Jasper Allen is a PhD student developing machine-learning-assisted approaches to reconstruct atomic structures from electron microscopy data by combining first principles simulations with experimental imaging.

PhD Research

Started: June 2026

Jasper’s research focuses on developing cross-correlative workflows that integrate electron microscopy, first principle simulations and machine learning to improve atomic scale structure determination. By combining experimental imaging with physics based modelling and machine learning techniques, his work aims to enable more reliable identification of atomic configurations that cannot be uniquely resolved from microscopy alone.

Research Approach

His work employs computational and data-driven models including:

• Machine Learning for Structure Reconstruction: Using machine learning models to infer atomic structure and composition from electron microscopy data
• First Principles Simulations: Applying DFT to evaluate structural stability and interpret experimental observations
• Experiment-Theory Integration: Developing workflows that combine STEM imaging, simulation, and atomistic modelling for improved structure characterisation

Background

Jasper completed an integrated Master’s degree in Physics at the University of Warwick. His master’s research involved developing machine learning techniques for particle interaction reconstruction in liquid argon time projection chambers. He is now part of the Maurer group at Warwick and collaborates with Diamond Light Source (ePSIC) as part of the HetSys CDT.

Research Impact

Improving the interpretation of atomic resoluton electron microscopy is essential for understanding the structure of complex materials. By combining experimental imaging with first principles simulations and machine learning, Jasper’s work aims to enable more reliable identification of atomic configurations. This supports the discovery and design of advanced materials for future technologies.