{
  "abstract": "Double-hybrid density functional theory (DHDFT) is the most accurate rung within the density-functional hierarchy, but its use for molecular structure and dynamics remains limited by the high computational cost of analytic nuclear gradients. Here, we present, the first GPU-accelerated implementation of analytic DHDFT gradients. The implementation, developed in the Extreme-scale Electronic Structure System (EXESS), employs resolution-of-the-identity (RI) techniques throughout the calculation and evaluates the complete relaxed gradient on GPUs, including the modified Z-vector response and the explicit nuclear derivative of the exchange–correlation potential. The distributed multi-GPU formulation is performanceportable across NVIDIA and AMD architectures, supports basis functions through g angular momentum, and enables calculations through quadruple-ζ basis quality. The implementation is validated against numerical derivatives and the established CPU implementation in ORCA for B2PLYP and revDSD-PBEP86D4. For sufficiently large calculations, EXESS sustains more than 50% of the theoretical peak double-precision floating-point performance and exhibits efficient strong scaling across multiple NVIDIA H200 GPUs and AMD MI250X GCDs. Compared with ORCA on a modern Sapphire Rapids CPU node, a single H200 GPU provides speedups ranging from approximately 10× to 675×. These results extend high-performance DHDFT from single-point energetics to efficient exploration of molecular potential-energy surfaces at the large basis-set quality required for accurate perturbative correlation.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computing",
    "Australian National University",
    "Monash Institute of Pharmaceutical Sciences",
    "Monash University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Ross Pure"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Giuseppe M. J. Barca"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/451k1rggsceey.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008642/v1",
  "fulltext_url": null,
  "id": "451k1rggsceey",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008642/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3107|Advanced Chemical Physics Studies"
    ]
  ],
  "openalex": "W7212136715",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008642/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/451k1rggsceey.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008642",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008642/v2"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Advanced Chemical Physics Studies",
      "subfield": "amo"
    }
  ],
  "title": "Making Analytic Double-Hybrid DFT Gradients Practical: A High-Performance Multi-GPU RI Implementation",
  "title_text": "Making Analytic Double-Hybrid DFT Gradients Practical: A High-Performance Multi-GPU RI Implementation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/451k1rggsceey",
  "venue": "ChemRxiv",
  "versions": []
}