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Julia Westermayr


Postdoctoral research fellow

I am a postdoctoral research fellow developing machine learning models to study photoplasmonic catalysis since Oct. 2020. My main goal is to enable computationally efficient and accurate nonadiabatic dynamics simulations by decoupling the costs of accurate quantum chemistry calculations from the dynamics simulations. Therefore, I aim to fit potential energy surfaces, forces, and related properties (dipole moments, nonadiabatic coupling vectors, electronic friction tensors,...) of molecules and materials based on first principle reference data.

In my project "Deep learning enhanced photoplasmonic catalysis of CO2", I aim to investigate the conversion of the greenhouse gas carbon dioxide into higher-valued resources, e.g., hydrocarbonds beyond carbon monoxide and methane, which can be used as sustainable feedstock for large-scale industrial applications. By using photplasmonic catalysis, noble metal nanoparticles can act as catalysts that use light as a green energy source to initiate chemical reactions of carbon dioxide at the molecule-metal interface. In this way, we also address the pressing problem of increasing levels of carbon dioxide in the atmosphere.

Reserach highlights:

During my PhD I developed machine learning models to advance excited-state molecular dynamics simulations. Our method is called SchNarc (DOI: 10.1021/acs.jpclett.0c00527) and has enabled nanosecond time scale photodynamics simulations (DOI: 10.1039/C9SC01742A) as well as the study of the excited amino acid tyrosine, in which we discovered roaming atoms for the first time in biologically relevant systems (arXiv: 2108.04373).

During my PostDoc I have developed a deep learning model to advance photoemission spectroscopy and to enable high-throughput screening (DOI: 10.1039/D1SC01542G) that should foster the search for novel functional organic molecules in the future.

10/2020 -


University of


  • UKRI Future Leaders Fellowship (Assoc.-Prof. Dr. Reinhard Maurer)

  • FWF Schrödinger fellowship, project: Deep-learning enhanced photoplasmonic catalysis of CO2

10/2017 -

09/2019 - 09/2020

PhD student

Assoc. Phd

University of Vienna

MolTag (FWF)

  • PhD Thesis Title "Machine Learning for Excited-State Molecular Dynamics Simulation"

  • Funding: uni:docs

  • Award: Sigrid-Peyerimhoff prize
  • Supervisor: Priv.-Doz. Dr. Philipp Marquetand, Univ.-Prof. Dr. Dr. h.c. Leticia González

2012 -

BSc. and MSc.

University of

  • Master Thesis Title "Modern UV-induced polymerization approaches for synthesizing Escherichia coli molecularly imprinted polymers"
  • Supervisor: Univ.-Prof. Dr. Peter Lieberzeit