{
  "abstract": "Density Functional Theory (DFT) is indispensable for materials science and drug discovery, yet the exchange--correlation (XC) evaluation remains a major bottleneck due to its cubic scaling. Although linear-scaling methods exploit electronic nearsightedness to reduce asymptotic complexity, they produce irregular sparse workloads that hide implicit sparsity and prevent efficient use of modern AI accelerators. We present MakoXC, a modular matrix-aligned XC evaluation engine that rearchitects nearsightedness-induced sparsity into regular, accelerator-friendly computations. MakoXC co-designs three key techniques: (1) Matrix-Aligned Cells reorganize nearsightedness-induced interactions into dense, accelerator-aligned data clusters; (2) Sparsity-Guided Activation translates deeper implicit sparsity into numerically correct structured execution for practical linear scaling; and (3) Kernel-Fused Pipeline consolidates fragmented workloads into a unified, compute-intensive execution path that fully unleashes accelerator throughput. Extensive evaluations show that MakoXC achieves average speedups of 67.8$\\times$ speedup over standard XC evaluation and 4.7$\\times$ over state-of-the-art linear-scaling methods. When integrated into a production-grade commercial DFT package, MakoXC scales XC evaluation to ubiquitin (1,231 atoms, def2-SVP) on 64 GPUs, enabling the end-to-end DFT calculation to complete in under five minutes. By restructuring XC evaluation into a unified, structured computation, MakoXC demonstrates how scientific workloads can achieve genuine low complexity while maximizing parallel efficiency on AI accelerators.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.01025",
  "author_total": 11,
  "authors": [
    {
      "name": "Haozhi Han"
    },
    {
      "name": "Fusong Ju"
    },
    {
      "name": "Jing Bai"
    },
    {
      "name": "Ruge Zhang"
    },
    {
      "name": "Xiang Zhao"
    },
    {
      "name": "Liang Yuan"
    },
    {
      "name": "Yunquan Zhang"
    },
    {
      "name": "Ting Cao"
    },
    {
      "name": "Liu Yunxin"
    },
    {
      "name": "Yifeng Chen"
    },
    {
      "name": "Kun Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9c6532dxy7vwr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.01025",
  "fulltext_url": null,
  "id": "9c6532dxy7vwr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.01025",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.DC"
    ],
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "openalex",
      "1708|Parallel Computing and Optimization Techniques"
    ]
  ],
  "openalex": "W7206188223",
  "pdf_url": "https://arxiv.org/pdf/2609.01025",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9c6532dxy7vwr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:31Z",
      "native_id": "2609.01025",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.01025"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "MakoXC: Rearchitecting DFT Exchange-Correlation with Matrix-Aligned and Knowledge-Organized Sparsity",
  "title_text": "MakoXC: Rearchitecting DFT Exchange-Correlation with Matrix-Aligned and Knowledge-Organized Sparsity",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:31Z",
  "url": "https://research.draconitic.com/paper/9c6532dxy7vwr",
  "venue": "arXiv",
  "versions": []
}