A structure-preserving neural density functional for the ions of a polymer electrolyte

Key points

• Predicting the structure and response of inhomogeneous polymer electrolytes requires a description of ion correlations that retains molecular-scale accuracy while remaining transferable across spatial scales and geometries.1
• The functional describes density profiles at an untrained salt concentration and predicts bulk structure factors and the long-wavelength number response.1
• Trained solely on planar density and internal-force profiles from molecular dynamics, the functional predicts ionic structure in larger domains and in two-dimensional external fields.1
• The spatial transferability provides a necessary foundation for connecting molecular correlations to continuum predictions at larger scales.1

Sources (1)

• [1]A structure-preserving neural density functional for the ions of a polymer electrolyte
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 03:22 PM

  We develop a neural density functional for electrolytes that preserves spatial symmetries, thermodynamic integrability and the Noether identities, with perfect screening recovered in stable, noncritical bulk states.

  Predicting the structure and response of inhomogeneous polymer electrolytes requires a description of ion correlations that retains molecular-scale accuracy while remaining transferable across spatial scales and geometries.

Extractive summary: sentences quoted from the sources.