Pith Number

pith:3DWC5SRL

pith:2026:3DWC5SRLNE2GWH54P3SVZTLMEU

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AIM2DAT: A Python-based Automated Ab Initio Material Modeling and Data Analysis Toolkit

Caterina Cocchi, Holger-Dietrich Sa{\ss}nick, Joshua Edzards, Timo Reents

The aim2dat Python package automates generation and analysis of large datasets from density functional theory calculations in materials research.

arxiv:2604.26551 v2 · 2026-04-29 · cond-mat.mtrl-sci

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\pithnumber{3DWC5SRLNE2GWH54P3SVZTLMEU}

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

we introduce the Automated Ab Initio Materials Modeling and Data Analysis Toolkit (aim2dat), a Python package offering a user-friendly interface to generate and handle big data, design high-throughput workflows based on density functional theory calculations, and analyze the output. Its key features include interfaces to online databases for structure query and analysis, high-throughput screening routines, and seamless integration of machine learning models.

C2weakest assumption

That the described interfaces to databases, DFT workflows, and machine learning models are robust, reliable, and easy-to-use in practice without significant integration errors or the need for extensive user customization.

C3one line summary

aim2dat is a new Python toolkit providing interfaces for database queries, high-throughput DFT workflows, and machine learning integration to handle large material datasets.

Receipt and verification

First computed	2026-06-23T03:13:57.445416Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d8ec2eca2b69346b1fbc7ee55ccd6c250cfe4e295aad04d1173cbb353c7232e7

Aliases

arxiv: 2604.26551 · arxiv_version: 2604.26551v2 · doi: 10.48550/arxiv.2604.26551 · pith_short_12: 3DWC5SRLNE2G · pith_short_16: 3DWC5SRLNE2GWH54 · pith_short_8: 3DWC5SRL

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3DWC5SRLNE2GWH54P3SVZTLMEU \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d8ec2eca2b69346b1fbc7ee55ccd6c250cfe4e295aad04d1173cbb353c7232e7

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "5e77412854ad9ec4a296f9ca081e93afa6e874a9ed366990907075dc724c7af8",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cond-mat.mtrl-sci",
    "submitted_at": "2026-04-29T11:33:41Z",
    "title_canon_sha256": "128d2b9778e2c335d5e274b524cffa2091e1a17556ee502e4fad4815fd4184af"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.26551",
    "kind": "arxiv",
    "version": 2
  }
}