{
  "url": "https://appliedfrontier.nl/en/research/signal-c37d4b7dab3a156e/",
  "title": "Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials",
  "published": "2026-10-29",
  "excerpt": "Atomistic simulations help researchers understand how molecules and materials behave, from chemical reactions and phase transitions to mechanical failure. These...",
  "content_sha256": "f4b293686211c30ab8a8933081040439c10dcd860dbf842109ddb69457e52574",
  "extracted_chars": 163,
  "artifact_links": [],
  "extraction_scope": "official_feed_metadata",
  "full_text_available": false,
  "feed_provider": "NVIDIA Developer",
  "id": "signal-c37d4b7dab3a156e",
  "org": "NVIDIA Developer",
  "topic": "AI models",
  "source_kind": "primary_technical_publication",
  "status": "unreviewed-source-report",
  "analysis_method": "deterministic_research_triage_v2",
  "novelty_verified": false,
  "proof_checked": false,
  "peer_review_verified": false,
  "matched_terms": [
    "AI models"
  ],
  "checked_at": "2026-11-01T13:17:06+00:00",
  "first_seen": "2026-10-31T19:32:56+00:00",
  "source_hash": "5cfec2a03724559afa096f34d6184b451488c64590759e7854189a6c82494947",
  "source_url": "https://developer.nvidia.com/blog/unlocking-tensor-core-performance-for-machine-learning-interatomic-potentials",
  "language": "en",
  "indexing": "noindex_until_editorially_enriched"
}