Publications of PN 6

  1. 2021

    1. V. Zaverkin and J. Kästner, “Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design,” Mach. Learn.: Sci. Technol., vol. 2, p. 035009, 2021, doi: 10.1088/2632-2153/abe294.
    2. A. M. Miksch, T. Morawietz, J. Kästner, A. Urban, and N. Artrith, “Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations,” Mach. Learn.: Sci. Technol., vol. 2, p. 031001, 2021, doi: 10.1088/2632-2153/abfd96.
    3. V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput., vol. 17, no. 10, Art. no. 10, 2021, doi: 10.1021/acs.jctc.1c00527.
  2. 2020

    1. V. Zaverkin and J. Kästner, “Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials,” J. Chem. Theory Comput., vol. 16, pp. 5410–5421, 2020, doi: 10.1021/acs.jctc.0c00347.
    2. G. Molpeceres, V. Zaverkin, and J. Kästner, “Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption,” Mon. Not. R. Astron. Soc., vol. 499, pp. 1373–1384, 2020, doi: 10.1093/mnras/staa2891.

Project Network Coordinators

This image shows Ingo Steinwart
Univ.-Prof. Dr. rer. nat.

Ingo Steinwart

[Photo: SimTech/Max Kovalenko]

Prof. Dr.

Daniel Weiskopf

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