{
  "abstract": "Advanced quantum mechanics/molecular mechanics (QM/MM), machine-learned interatomic potentials (MLIPs), and hybrid methods provide powerful engines for modeling solution-phase chemistry. Yet before production begins, chemically diverse explicit-solvent systems must be parameterized, assembled at the desired composition, relaxed, equilibrated, and sampled. This preparatory “first mile” remains labor-intensive, requiring multiple software packages and manual customization, particularly for transition-metal complexes, solvent mixtures, and multicomponent electrolytes. Here, we present AutoSolvate 2.0, a modular, open-source framework that converts molecular structures and solution compositions into equilibrated, simulation-ready explicit-solvent ensembles. The new AutoMCPB module automates Metal Center Parameter Builder (MCPB.py)-based parameterization of transition-metal complexes (TMCs), while generalized system representations support multiple solutes and solvents at arbitrary molar, weight, or volume ratios. Standardized wrappers integrate force-field generation, box construction, minimization, heating, equilibration, classical sampling, and microsolvated-cluster extraction. Native classical molecular dynamics (MD) and TeraChem-based QM/MM workflows execute directly; equilibrated coordinates, velocities, trajectories, and clusters provide starting points for external quantum mechanical (QM), other QM/MM, MLIP training, and hybrid machine learning (ML)/classical workflows. JSON, guided command-line, and Model Context Protocol interfaces support high-throughput and agent-assisted use. We demonstrate these capabilities by reproducing the solvent-dependent absorption spectrum of an aqueous transition-metal photosensitizer, the composition-dependent microsolvation of water–acetonitrile mixtures, and characteristic ion-pairing features of a published multicomponent electrolyte simulation. Together, these applications establish AutoSolvate 2.0 as a preparation and initial-sampling layer connecting chemical system specification to classical, quantum, and machine-learning simulations. Rather than competing with higher-accuracy methods, AutoSolvate provides the on-ramp needed to apply them efficiently to complex explicit-solvent systems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Sangni Xun"
    },
    {
      "name": "Fangning Ren"
    },
    {
      "name": "Pinyuan Li"
    },
    {
      "name": "Xu Chen"
    },
    {
      "name": "Lechen Dong"
    },
    {
      "name": "Bushra Alam"
    },
    {
      "name": "Fang Liu",
      "orcid": "0000-0003-1322-4997"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0vcfsc2z2n535.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15007305/v2",
  "fulltext_url": null,
  "id": "0vcfsc2z2n535",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15007305/v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3107|Advanced Chemical Physics Studies"
    ]
  ],
  "openalex": "W7221694348",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15007305/v2",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/0vcfsc2z2n535.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15007305",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15007305/v2"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Advanced Chemical Physics Studies",
      "subfield": "amo"
    }
  ],
  "title": "AutoSolvate 2.0: Automated Preparation of Chemically Diverse Explicit-Solvent Systems for Multiscale Molecular Simulation",
  "title_text": "AutoSolvate 2.0: Automated Preparation of Chemically Diverse Explicit-Solvent Systems for Multiscale Molecular Simulation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/0vcfsc2z2n535",
  "venue": "ChemRxiv",
  "versions": []
}