Using Machine Learning methods to obtain the properties of water
DIPC Seminars
 Speaker

Alexandre Reily Rocha, Instituto de Física Teórica (IFT), Universidade Estadual Paulista (UNESP), São Paulo, Brazil
 When

2022/10/24
14:00  Place
 Hybrid Seminar: Donostia International Physics Center
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Water is one of the most fundamental substances know to us, however some
properties are not yet understood. This comes from the interplay between long
and short range interactions, which give rise to a plethora of phenomena in
water. First principles calculations are a powefull tool to describe different
effects in water. Nevertheless, the high computational cost is a problem for
systems with a large number of molecules. An alternative is to use neural
networks trained with DFT (density functional theory) calculations. With this
metodology we can describe the molecular interactions of small systems using
DFT and apply them to much larger systems, and for longer timescales.
Thus, using molecular dynamics (MD) with a neuralnetwork trained force field
we describe a number of properties of water  both dynamical and static 
comparing different exchange and correlation potentials. We show that even
simple PBEtype potentials with van der Waals corrections can yield very good
results. At the end of my talk I will also discuss methods to obtain the
smallest possible data set used for training.
Host: Thomas Frederiksen
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