Best poster award for Félix Musil
Félix Musil won a poster prize at the workshop "Developing High-Dimensional Potential Energy Surfaces – From the Gas Phase to Materials" in Göttingen, for his poster on "Atom-density representations for machine learning". Congratulations!
The applications of machine learning techniques to chemistry and materials science become more numerous by the day. The main challenge is to devise representations of atomic systems that are at the same time complete and concise, so as to reduce the number of reference calculations that are needed to predict the properties of different types of materials reliably. A recent paper by COSMO researchers Félix Musil and Michael Willatt introduced an abstract definition of chemical environments that is based on a smoothed atomic density, highlighting the connections between some popular choices of representations for describing atomic systems.
A poster presenting this work has been awarded a prize (sponsored by the Journal of Chemical Physics) at the workshop on "Developing High-Dimensional Potential Energy Surfaces – From the Gas Phase to Materials", at the University of Göttingen.
NCCR MARVEL - Funded by the SNSF
Michael J. Willatt, Félix Musil and Michele Ceriotti, "Atom-density representations for machine learning", J. Chem. Phys. 150, 154110 (2019); https://doi.org/10.1063/1.5090481