{
  "id": 16927,
  "url": "https://arxiv.org/abs/2608.04942v1",
  "title": "CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications",
  "summary": "CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications. CheMLFlow targets a common bottleneck in scientific machine learning development, where researchers often need to assemble data acquisition, curation, representation, model training, validation, screening, interpretation, and reporting into a reproducible pipeline, even when their primary research contribution concerns only one stage. C",
  "authors": "Brendan Smith, Susana Lopez-Moreno, Eric Dolores-Cuenca, Sangil Kim, Jose L. Mendoza-Cortes, Nijamudheen Abdulrahiman",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T15:08:29.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16927",
  "original_url": "https://arxiv.org/abs/2608.04942v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}