The photocurrent reached 1.1 mA cm⁻² for Sn/Mg co-doping which relieved the α-Fe₂O₃ lattice distortion and increased the doping concentration.

The prediction of steel’s properties by the process parameters has been of great interest because they can effectively reduce production costs and improve product quality. At present, the extensively used artificial neural network (ANN) can only reveal the correlation between parameters and mechanical properties from the perspective of statistics but loses the critical information on time-series correlation in the steel production process. In this work, time-series neural networks based on long short-term memory (LSTM) were established to predict the steel plate’s yield strength (YS), ultimate tensile strength (UTS), and elongation (EL). The results verified that the proposed LSTM model exhibited sufficient accuracy and outperformed the classical algorithms (SVM, random forest, ANN), with mean squared error reduced by 30% when compared with ANN. Also, the sensitivity analysis proved that LSTM was more capable to make full use of the information contained in input parameters and achieved better generalization performance. Notably, the interpretation of the results was more consistent with the real physical metallurgical process of a hot-rolled steel plate and can provide a better understanding of the physical metallurgical process.