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Predicting the hydrogen uptake ability of a wide range of zeolites utilizing supervised machine learning methods

Seyed Mehdi Alizadeh, Zahra Parhizi, Ali Hosin Alibak, Behzad Vaferi, Saleh Hosseini

2022International Journal of Hydrogen Energy31 citationsDOI

Topics & Concepts

Artificial neural networkAbsolute deviationSigmoid functionMean squared errorLeverage (statistics)Root mean squareRange (aeronautics)LogarithmTangentMean absolute percentage errorRelative standard deviationComputer scienceArtificial intelligenceMathematicsStatisticsMaterials sciencePhysicsMathematical analysisQuantum mechanicsComposite materialDetection limitGeometryHydrogen Storage and MaterialsZeolite Catalysis and SynthesisMetal-Organic Frameworks: Synthesis and Applications
Predicting the hydrogen uptake ability of a wide range of zeolites utilizing supervised machine learning methods | Litcius