Stacking classical-quantum hybrid learning approach for corrosion inhibition efficiency of N-heterocyclic compounds

This study introduces the stacking classical-quantum model (SCQM) as a novel approach to predicting N-heterocyclic compounds' corrosion inhibition efficiency (CIE). SCQM integrates classical models such as Multi-Layer Perceptron Neural Network (MLPNN) and Random Forest (RF) as base learners, with Qu...

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Hlavní autoři:	Muhamad Akrom, Supriadi Rustad, Totok Sutojo, Wahyu Aji Eko Prabowo, Hermawan Kresno Dipojono, Ryo Maezono, Hideaki Kasai
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Elsevier 2025-01-01
Edice:	Results in Surfaces and Interfaces
Témata:	Stacking model Quantum machine learning Corrosion inhibition N-heterocyclic
On-line přístup:	http://www.sciencedirect.com/science/article/pii/S2666845925000492
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