alex squires

Computational materials & defect chemistry

I’m group leader in the Scanlon Materials Theory Group (School of Chemistry, University of Birmingham), where I investigate defective and disordered materials through large-scale simulations. Before this, I was a research fellow on the Faraday Institution’s FutureCat programme. Two of my studies from that period were highlighted as exemplar impact case studies:

Alongside my research, I have been closely involved in scientific software development—leading or contributing to:

  • py-sc-fermi — self-consistent calculation of Fermi levels and defect concentrations from electronic-structure data.
  • doped — automated, reproducible workflows for charged-defect supercell calculations.
  • kinisi — Bayesian analysis of ionic transport from molecular-dynamics simulations.

My teaching spans undergraduate to PhD level, covering chemistry, computational methods, and data analysis. I’ve supervised research projects at multiple stages of study and provided guidance for doctoral researchers and postdocs, with a focus on developing practical skills in computational materials science.

In 2025, I led the conception and delivery of the inaugural Solid Data Summer School — a workshop on managing and analysing data in solid-state chemistry. Designed to bridge data science and materials chemistry, the programme combined lectures with hands-on exercises, giving participants the tools to work confidently with data-intensive workflows.

Beyond academia, I’ve run coding workshops for young people through the Raspberry Pi Foundation’s CoderDojo programme and delivered science enrichment courses in schools to broaden access to higher education. Across these settings, I aim to foster curiosity, build technical confidence, and show how data-driven science can address real-world challenges.