ArticlePrecision chemistry2026
ResNet-KAN for Small-Sample Chemical Formulation Prediction: Hybrid Architecture and Interpretability Analysis.
Article in Precision chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Formulation property prediction in industrial chemistry is often hindered by severe data scarcity due to high experimental costs and confidentiality constraints, which leads to overfitting and unreliable evaluation. Here, we propose a hybrid ResNet-KAN framework for small-sample formulation modeling and demonstrate it on proprietary emulsion oil formulations (32 real formulations, 11 anonymized components) for predicting pH and defoaming performance. To improve data efficiency while preventing metric inflation, we integrate fold-wise CTGAN augmentation into a repeated nested cross-validation protocol, where synthetic (
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