From handbooks to high-throughput: rule-based prediction of electronic absorption maxima from SMILES with ChromoPredict

Automated Woodward–Fieser–Kuhn rule implementation bridges classical heuristics with modern predictive and interpretable machine learning tools.

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Publication Details

Journal
Digital Discovery
Published
2025-11-06
DOI
https://doi.org/10.1039/d5dd00382b
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

From handbooks to high-throughput: rule-based prediction of electronic absorption maxima from SMILES with ChromoPredict

C. Förster, Carolin Müller
Digital Discovery
Machine Learning in Materials Science
article

From handbooks to high-throughput: rule-based prediction of electronic absorption maxima from SMILES with ChromoPredict

C. Förster, Carolin Müller
article en

Abstract

Automated Woodward–Fieser–Kuhn rule implementation bridges classical heuristics with modern predictive and interpretable machine learning tools.

Digital DiscoveryVol. 5(1)
Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
Friedrich-Alexander-Universität Erlangen-Nürnberg
Openalex Percentile: Top 74%
Machine Learning in Materials Science
DOIOpen Access PDFOpenAlex
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From handbooks to high-throughput: rule-based prediction of electronic absorption maxima from SMILES with ChromoPredict — C. Förster, Carolin Müller · Digital Discovery (2025) | TGRS Research Map | TGRS