{
  "abstract": "Bisphenol A-glycidyl methacrylate (Bis-GMA) is widely used in dental resins, but its BPA-derived chemistry motivates the search for structurally distinct monomers that retain favorable cured-network properties. Here we introduce EloKriti, an active-learning workflow that links molecular candidate representations to explicit in silico curing and multi-objective optimization of network-emergent properties. We showcase EloKriti by navigating a curated 1000-candidate BPA-free chemical space, filtered from Open Macromolecular Genome for compatibility with vinyl chain-growth polymerization, to simultaneously maximize elastic modulus and minimize volumetric shrinkage and fractional free volume. The retrospectively normalized dominated hypervolume increased from 0.371 to 0.590 by iteration 6, when 57 candidates had been successfully evaluated; although additional candidates were evaluated in the next two rounds, the hypervolume and Pareto-set size did not increase further. From the resulting 13-member Pareto front, three chemically distinct backbones were subsequently incorporated into established, industry-relevant resin building blocks through candidate-specific reactive-site mapping consistent with the growth chemistry of each building block. The resulting synthetically actionable monomers were subjected to five-replica molecular-dynamics virtual-curing validation, and all three networks successfully reached the 95% target degree of conversion. These virtually cured networks exhibited highly specialized property profiles: one optimized the density–shrinkage–free-volume balance, another maximized elastic modulus, and the third achieved the highest glass-transition temperature. These results provide synthetically actionable BPA-free leads for future experimental evaluation and a data-efficient route for prioritizing chemistries on the basis of properties that emerge only after network formation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Material Design and Innovation",
    "University at Buffalo",
    "Department of Restorative Dentistry"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Sai Harshit Balantrapu"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Sabrina Feitosa Sochacki"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Ganesh Sivaraman",
      "orcid": "0000-0001-9056-9855"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0z8kaec3hfh7r.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008229/v1",
  "fulltext_url": null,
  "id": "0z8kaec3hfh7r",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008229/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",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7207783913",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008229/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/0z8kaec3hfh7r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008229",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008229/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Accelerated Discovery of BPA-Free Dental Resin Candidates through Active Learning of Network-Emergent Properties",
  "title_text": "Accelerated Discovery of BPA-Free Dental Resin Candidates through Active Learning of Network-Emergent Properties",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/0z8kaec3hfh7r",
  "venue": "ChemRxiv",
  "versions": []
}