Matej Praprotnik

National Institute of Chemistry, SI



Matej Praprotnik is Head of the Laboratory for Molecular Modeling at the National Institute of Chemistry and professor of Physics at the Faculty of Mathematics and Physics, University of Ljubljana. He studied physics and received his B.S. and Ph.D. degrees from the University of Ljubljana. He carried out postdoctoral research in the Theory group at the Max Planck Institute for Polymer Research, Mainz, Germany.
Matej is former Chair of the PRACE Scientific Steering Committee. He served as President of the Slovenian Biophysical Society from 2015 to 2019. He also serves as a council member of CECAM and as a scientific council member of the National Institute of Chemistry. He is recipient of the ERC Advanced Grant 2019 by the European Research Council and the project coordinator of MultiXscale, a EuroHPC JU Center of Excellence. His research is focused on computer simulation of soft and biological matter. The focus is on developing and combining innovative computational and theoretical methods augmented by machine learning techniques to study complex molecular systems.


Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media


Molecular simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules.
At the same time, the models should be computationally efficient to enable simulations of large spatiotemporal scales. In this talk, I will present a deep implicit solvation model for sodium chloride solutions that satisfies both requirements.
Owing to the use of a neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive.
I will demonstrate the aplicability of our approach for pure ionic solutions and a solvated DNA molecule. In both cases, the structural properties are in good agreement with all-atom molecular simulations, showcasing a general methodology for the efficient and accurate modeling of ionic media.
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