Dr. Sebastian Mai
Institut of theroetical chemistry, University of Vienna
will speak on the topic
Machine Learning the Ab Initio Accuracy: Application to Battery Materials - online
Abstract:
Traditionally, the molecular condensed phase has been modelled using classical force fields which are computationally affordable, but often lack the accuracy required for predictive modelling. Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for studying molecular mechanisms in the condensed phase, however, they are too expensive to capture many key thermodynamic and transport properties that converge slowly with respect to simulation length and time scales. Machine learning (ML) approaches which reach the accuracy of ab initio simulation, and
which are, at the same time, sufficiently affordable hold the key to bridging this gap. One of the main challenge with ML modelling the molecular condensed phase is posed by the separation of scales between intra- and inter-molecular interactions. This presentation will discuss strategies for quantifying the relative accuracy of inter-molecular interactions as well as iterative training protocols for obtaining stable ML models. Specifically, we will look at results for the Ethylene Carbonate : Ethyl Methyl Carbonate binary liquid solvent, a key component of liquid electrolytes in rechargeable Li-ion batteries.
Machine Learning Force Fields for Molecular Liquids: Ethyle Carbonate / Ethyl Methyl Carbonate Binary Solvent
Magdău, I. B., Arismendi-Arrieta, D. J., Smith, H. E., Grey, C. P., Hermansson, K., and Csányi, G.
NPJ Computational Materials, accepted. (2023)
Interested parties are cordially invited
Professor Dr. Kühn