{
  "abstract": "Liquid chromatography (LC) is essential to organic synthesis and emerging selfdriving laboratories (SDLs), yet LC method development remains cost-intensive and dependent on expert intuition. Mechanistic chromatographic models offer a route to transferable and data-efficient method development, but their practical use is limited by the computational cost of inverse modeling and by the ill-posedness of parameter estimation from experimental chromatograms. Here we report a wet-lab-validated differentiable physics framework for LC inverse modeling. The framework integrates mechanistic column modeling with physics-informed machine learning (PIML) to enable efficient forward simulation and gradient-based inverse estimation of molecular transport and adsorption parameters from gradient-elution chromatograms. The primary PINN surrogate reproduces the mechanistic reference with a root-mean-square error of 10−3 in dimensionless concentration. To address the ill-posed nature of the inverse problem, we introduce an observability-guided experimental-design strategy that quantifies parameter identifiability and incorporates this information into the inverse objective to promote convergence toward the ground-truth parameters. Across 300 dry-lab simulation parameter combinations, the proposed strategy recovered the ground-truth parameters with relative errors ≤ 10% in 88.3% of cases using three gradient programs, compared with only 6.7% using a single gradient program. We further validate the framework against wet-lab LC 1 measurements, achieving an absolute retention-time error ≤ 10 s for 89.3% of samples and establishing a wet-lab database comprising 731 sets of transport and adsorption parameters extracted from 2,553 gradient-elution LC chromatograms. This work provides an observability-aware differentiable modeling foundation for autonomous LC method development and closed-loop analytical decision-making in AI-driven laboratories.",
  "abstract_shown": true,
  "affiliations": [
    "ChemLex",
    "School of Data Science",
    "Chinese University of Hong Kong"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Baochen Li",
      "orcid": "0009-0004-7221-5075"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Zihan Zhou",
      "orcid": "0009-0006-6107-6190"
    },
    {
      "affs": [
        0
      ],
      "name": "Yanqing Yu"
    },
    {
      "affs": [
        0
      ],
      "name": "Sen Lin"
    },
    {
      "affs": [
        0
      ],
      "name": "Peng Wang"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Tianshu Yu",
      "orcid": "0000-0002-6537-1924"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoxue Wang",
      "orcid": "0000-0002-7231-9841"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ekbexjcrtfzvr.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009352/v1",
  "fulltext_url": null,
  "id": "ekbexjcrtfzvr",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009352/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",
      "1607|Analytical Chemistry and Chromatography"
    ]
  ],
  "openalex": "W7214085388",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009352/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/ekbexjcrtfzvr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009352",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009352/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1607|Analytical Chemistry and Chromatography",
      "subfield": "physical"
    }
  ],
  "title": "Differentiable mass transfer modeling enables wet lab validated inverse analysis of liquid chromatography",
  "title_text": "Differentiable mass transfer modeling enables wet lab validated inverse analysis of liquid chromatography",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/ekbexjcrtfzvr",
  "venue": "ChemRxiv",
  "versions": []
}