Samuel J. Hall

Samuel J. Hall

Postdoctoral Asisstant

Helmholtz-Zentrum Berlin

Biography

I am Dr. Sam Hall, a postdoctoral researcher at Helmholtz-Zentrum Berlin. My current work is focused on developing machine learning models for x-ray spectroscopy.

My background and interests are in computational spectroscopic simulations, mainly x-ray photoelectron spectroscopy, (XPS) and x-ray absorption spectroscopy (XAS). I specialise in using density functional theory (DFT) in order to calculate core-level spectroscopy of metal-organic interfaces. These types of spectra can often consist of overlapping features and significant broadening, making interpretation difficult. Through the use of first-principle simulations, I have been able to decompose spectra in terms of both atomic contributions and molecular orbital contributions. I have also looked into characterising how the interaction between the molecule and the metal surface changes spectra and highlighting how the different levels of interaction change spectra.

Interests
  • X-ray Spectroscopy (XPS, XAS)
  • Density Functional Theory
  • Molecule-Metal Interfaces
  • Graph Neural Networks
Education
  • PhD in Molecular Analytical Science, 2022

    University of Warwick

  • MSc in Molecular Analytical Science, 2018

    University of Warwick

  • MChem in Chemistry, 2016

    University of Leicester

Skills

Technical
Python Python
Fortran Fortran
GitHub Github
Machine Learning
PyTorch Pytorch
PyG Pytorch Geometric
NetworkX NetworkX

Experience

 
 
 
 
 
Helmholtz-Zentrum Berlin
Postdoctoral Researcher
March 2023 – Present Berlin, Germany
Machine learning development of models for the prediction of X-ray adsorption spectroscopy (XAS), with a focus on using graph neural networks for XAS prediction of graphene oxide nanoflakes.
 
 
 
 
 
University of Warwick
Postdoctoral Researcher
March 2022 – January 2023 Coventry, United Kingdom
Software development of the all-electron electronic structure code FHI-aims. Improvement of the core-hole calculation code used for such applications as simulating X-ray photoelectron and X-ray adsorption spectroscopy. The code was brought up-to-date, improving the scaling and efficiency whilst also restructuring and simplifying the code for future development.

Accomplish­ments

Coursera
Advanced Learning Algorithms
See certificate
Coursera
Supervised Machine Learning: Regression and Classification
See certificate

Recent Publications

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(2022). Future Directions: General Discussion. Faraday Discuss..

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(2022). Topological Stone–Wales Defects Enhance Bonding and Electronic Coupling at the Graphene/Metal Interface. ACS Nano.

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(2022). Coexistence of Carbonyl and Ether Groups on Oxygen-Terminated (110)-Oriented Diamond Surfaces. Commun. Mater..

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