Molecular Dynamics Simulations of Complex Systems Using High-Dimensional Neural Networks

• Applicant: Professor Dr. Jörg Behler
• Subject Area: Theoretical Chemistry: Electronic Structure, Dynamics, Simulation
• Term: from 2016 to 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 329898176

• Final Report Year: 2019

Final Report Abstract

No abstract available

Publications

• First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems. Angewandte Chemie, International Edition, Vol. 56. 2017, Issue 42, pp. 12828-12840.
  J. Behler
  (See online at https://doi.org/10.1002/anie.201703114)
• Proton Transfer Mechanisms at the Water-ZnO Interface: The Role of Presolvation. Journal of Physical Chemistry Letters, Vol. 8. 2017, Issue 7, pp. 1476–1483.
  V. Quaranta, M. Hellström, J. Behler
  (See online at https://doi.org/10.1021/acs.jpclett.7b00358)
• Proton-Transfer-Driven Water Exchange Mechanism in the Na+ Solvation Shell. Journal of Physical Chemistry B, Vol. 121. 2017, Issue 16, pp. 4184–4190.
  M. Hellström, J. Behler
  (See online at https://doi.org/10.1021/acs.jpcb.7b01490)
• Maximally Resolved Anharmonic OH Vibrational Spectrum of the Water/ZnO(10-10) Interface from a High-Dimensional Neural Network Potential. Journal of Chemical Physics, Vol. 148. 2018, Issue 24, 241720.
  V. Quaranta, M. Hellström, J. Behler, J. Kullgren, P. Mitev, K. Hermansson
  (See online at https://doi.org/10.1063/1.5012980)
• Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations. Journal of Physical Chemistry B, Vol. 122. 2018, Issue 44, pp. 10158–10171.
  M. Hellström, M. Ceriotti, J. Behler
  (See online at https://doi.org/10.1021/acs.jpcb.8b06433)
• Ab initio Thermodynamics of Liquid and Solid Water. PNAS, Vol. 116. 2019 no. 4, pp. 1110-1115.
  B. Cheng, E. A. Engel, J. Behler, C. Dellago, M. Ceriotti
  (See online at https://doi.org/10.1073/pnas.1815117116)
• From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5. Journal of Chemical Theory and Computation, Vol. 15. 2019, Issue 6, pp. 3793-3809.
  M. Eckhoff, J. Behler
  (See online at https://doi.org/10.1021/acs.jctc.8b01288)
• One-Dimensional vs. Two-Dimensional Proton Transport Processes at Solid-Liquid Zinc-Oxide-Water Interfaces. Chemical Science, Vol. 10. 2019, pp. 1232-1243.
  M. Hellström, V. Quaranta, J. Behler
  (See online at https://doi.org/10.1039/c8sc03033b)
• Parallel Multi-Stream Training of High-Dimensional Neural Network Potentials. Journal of Chemical Theory and Computation, Vol. 15. 2019, Issue 5, pp. 3075–3092.
  A. Singraber, T. Morawietz, J. Behler, C. Dellago
  (See online at https://doi.org/10.1021/acs.jctc.8b01092)
• Structure and Dynamics of the Liquid-Water/Zinc-Oxide Interface from Machine Learning Potential Simulations. Journal of Physical Chemistry C, Vol. 123.2019, Issue 2, pp. 1293–1304.
  V. Quaranta, J. Behler, M. Hellström
  (See online at https://doi.org/10.1021/acs.jpcc.8b10781)