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Julio Brito Santana

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Open access Aug 2026

A Leakage-Aware Benchmark Study of Machine Learning Models for Deep Eutectic Solvent Property Prediction

This study provides a structured and reproducible assessment of the conditions under which descriptor-based ML models can be expected to succeed or fail in DES systems and highlights the importance of rigorous, leakage-aware evaluation in data-driven chemical modeling.

Hakim Faraji, Julio Brito Santana, R. Rodríguez-Ramos et al. · 0 citations