All Stories

  1. sGDML: Constructing accurate and data efficient molecular force fields using machine learning
  2. Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
  3. Towards exact molecular dynamics simulations with machine-learned force fields
  4. Mechanical Vibrations of Atomically Defined Metal Clusters: From Nano- to Molecular-Size Oscillators
  5. SchNet – A deep learning architecture for molecules and materials
  6. Machine learning of accurate energy-conserving molecular force fields
  7. Vibrational properties and specific heat of core–shell Ag–Au icosahedral nanoparticles
  8. Structural Determination of Metal Nanoparticles from their Vibrational (Phonon) Density of States
  9. Size and Shape Dependence of the Vibrational Spectrum and Low-Temperature Specific Heat of Au Nanoparticles
  10. Vibrational Spectrum, Caloric Curve, Low-Temperature Heat Capacity, and Debye Temperature of Sodium Clusters: The Na139+ Case
  11. Vibrational Properties of Metal Nanoparticles: Atomistic Simulation and Comparison with Time-Resolved Investigation