Sam Shepherd

Computational materials scientist with a PhD in machine learning for clay mineral systems and postdoctoral experience applying metadynamics to model endocrine disrupting compound behaviour on carbon nanotubes. I am skilled in creating data driven workflows and aligning with physics led approaches to characterize molecular interactions in complex materials and environmental systems. Experienced in Python, LAMMPS, PLUMED, and I-PI, with publications showcasing my ability to setup, organise, and investigate complex atomistic simulations and provide insights on adsorption free energy and material properties.

I am now seeking to apply this combined machine learning and molecular simulation expertise.

Postdoctoral Researcher at Queen's University Belfast (2024-06 – 2026-03)

Under the project title of 'Investigating the adsorption of endocrine disruptors using carbon based materials', I was the lead computational researcher. I created reproducible workflows and documentation detailing computational choices regarding the methodology and materials used in the research project. I developed, tested, and troubleshot a large amount of LAMMPS simulations involving several complex organic molecules and used metadynamics to investigate the adsorption free energy and some details on the optimal adsorption configurations of a large range of molecules.

Additionally, I developed code utilities to study similar adsorption profiles on clay minerals.

PhD in Physics – Queen's University Belfast (2020 – 2024)

Master's Degree in Chemistry – Queen's University Belfast (2016 – 2020)

A-Levels in Physics, Chemistry, Maths – Portadown College (2016 – 2016)