Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks

Research article (The Journal of Chemical Physics, 2022) · cited 10× · AI/ML
Press Enter · cited answer in seconds

Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks

Summary

Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks is a scholarly article[1].

Key Facts

  • Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks's instance of is recorded as scholarly article[2].

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks. Retrieved May 24, 2026, from https://4ort.xyz/entity/gaussian-approximation-of-dispersion-potentials-for-efficient-featurization-and-machine-learning-predictions-of-metalorg
MLA “Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/gaussian-approximation-of-dispersion-potentials-for-efficient-featurization-and-machine-learning-predictions-of-metalorg.
BibTeX @misc{4ortxyz_gaussian-approximation-of-dispersion-potentials-for-efficient-featurization-and-machine-learning-predictions-of-metalorg_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks}}, year = {2026}, url = {https://4ort.xyz/entity/gaussian-approximation-of-dispersion-potentials-for-efficient-featurization-and-machine-learning-predictions-of-metalorg}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks — https://4ort.xyz/entity/gaussian-approximation-of-dispersion-potentials-for-efficient-featurization-and-machine-learning-predictions-of-metalorg (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/gaussian-approximation-of-dispersion-potentials-for-efficient-featurization-and-machine-learning-predictions-of-metalorg · Last refreshed: