• Room LG.022 - New Physics

    United Kingdom

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Personal profile

Research Interests

There are two interlinked facets to my current research:


  • Understanding the structure, dynamics and spectroscopic properties of water and aqueous systems (particularly interfaces) using atomistic simulations. In particular, I use methods based on the imaginary-time Feynman path integral picture to account for the effects of quantum-mechanics on the motion of the nuclei.
  • The development and application of supervised machine-learning methods, particularly symmetry-adapted methods that allow the efficient and accurate prediction of molecular and materials properties transforming like a tensor (e.g. dipole moment, polarizability ...). See alphaml.org for an example.


External links:

  • My Google Scholar profile
  • AlphaML Web App -- predict molecular polarizabilities with better-than-density functional theory accuracy
  • TENSOAP -- python code for symmetry-adapted regression of tensors


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