Dr Julia Melisande Fischer1, Ms Michelle Hunter2, Dr Marlies Hankel2, Prof Debra Searles2,3, Dr Amanda Parker1, Dr Amanda Barnard1
1Data61 CSIRO, Docklands , Australia, 2Centre for Theoretical and Computational Molecular Science, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia, 3School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
Most machine learning research on chemical systems study either materials, with periodic structures, or molecules. Often for surface catalysis, the relevant information is in-between both systems. For this study, our dataset contains molecules on different surface defects, namely catalytic active centres. These are N-doped graphene pores supporting one or two single metal atoms.
The binding energies (BE) is an indicator for the strength of the interaction of the molecule with the surface and is used as the label. The data includes around 1600 structures of molecules on various different active centres and their BE. The geometric data comprises of the 3-dimensional coordinates of all atoms in a periodic cell. The main problem is describing features which are meaningful for a wide number of structures, as different structures have different atom types, adsorbed species and number of atoms. Through different descriptions of the geometrical structure in the systems, we try to link the BE to essential geometric features.
Meli Fischer completed her Bachelor and Master of Science at Ulm University, Germany. She specialised on physical and theoretical chemistry with an emphasis on material science. For her further studies, she received an international postgrad scholarship from the University of Queensland and graduated with a Doctor of Philosophy in chemistry in 2018. Since January 2019, she is working on material data science at Data61, Docklands.