arXiv (All)
open access
Native implementation of the machine-learned Skala exchange-correlation functional in CP2K: Unified one-centre reconstruction for molecular and condensed-phase calculations
chemical-physicscomputational-physicsdisordered-systems-and-neural-networksmaterials-sciencequantum-physics
We implement the machine-learned Skala exchange-correlation (XC) functional natively in CP2K using its Gaussian and plane-wave (GPW) and Gaussian and augmented-plane-wave (GAPW) methods. A joint one-centre reconstruction of density, density gradients, and kinetic-energy density before functional eva
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Molecular Implementation of the Machine-Learned Skala Exchange-Correlation Functional in CP2K through GauXC#1128634
- Phase-space invariants as indicators of the critical behavior of nanoaggregates#934944
- Highly Parallel Real-Space Random Phase Approximation Using Lanczos Quadrature and Interpolation#1110768
- Towards improved simulations of self-organising molecular materials#343993
- A Symmetry-Constrained Fourier--Morse Framework for Compact Anisotropic Interaction Potentials#1034777
Record · ID 1119128
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive