A chemical bond-based machine learning model for dipole moment: Application to dielectric properties of liquid methanol and ethanol

Ejemplares similares

• Transferability of the chemical bond-based machine learning model for dipole moment: the GHz to THz dielectric properties of liquid propylene glycol and polypropylene glycol
  por: Amano, Tomohito, et al.
  Publicado: (2024)
• Generator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-scale Simulations
  por: Matsumura, Naoki, et al.
  Publicado: (2024)
• Anharmonic phonon renormalization and thermal transport in the type-I Ba$_{\rm 8}$Ga$_{\rm 16}$Sn$_{\rm 30}$ clathrate from first principles
  por: Ohnishi, Masato, et al.
  Publicado: (2021)
• Nuclear quantum effect on the elasticity of ice VII under pressure: A path-integral molecular dynamics study
  por: Tsuchiya, Jun, et al.
  Publicado: (2023)
• Data-Assimilated Crystal Growth Simulation for Multiple Crystalline Phases
  por: Kubo, Yuuki, et al.
  Publicado: (2024)