Predictive modelling

Linear regression

Catalysis

Perceptron

Biology

Statistics

Metallurgy

Materials science

Molecular descriptor

Quantum chemical

Molecule

Computer science

Levenberg–Marquardt algorithm

Organic chemistry

Mathematics

Quantitative structure–activity relationship

Artificial intelligence

Ionic bonding

Corrosion

Chemistry

Ionic liquid

Artificial neural network

Biological system

Mean squared error

Machine learning

Multilayer perceptron

Computational Materials Science

The present study reports the quantum chemical studies and quantitative structure activity relationship (QSAR) modeling of thirty ionic liquids utilized as chemical additives to repress mild steel degradation in 1.0 M HCl. Five molecular descriptors obtained from standardization of calculated descriptors together with the inhibitor concentration were employed in model building. Multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN) modeling were utilized in model construction. The optimal MLPNN model was developed using a network architecture of 6-3-5-1 with Levenberg-Marquardt as the learning algorithm. The model yielded an MSE of 29.9242, RMSE of 5.4703, MAD of 4.9628, MAPE of 5.7809, rMBE of 0.1202 and CoV of 0.0052. The MLPNN model displayed better predictive performance than the MLR model. Furthermore, developed models were applied to forecast the inhibition efficiencies of five novel ionic liquids. The theoretical inhibitors were found to be effective inhibitors of steel corrosion, showing over 80% inhibition efficiency.

Multilayer perceptron neural network-based QSAR models for the assessment and prediction of corrosion inhibition performances of ionic liquids