As foremost sensors of ambient conditions, temperature sensors are regarded as the most vital ones in
wide-ranging applications touching the societal life. Amongst the temperature sensors, NTC thermistors
have captured their unique place due to the favorable metrics such as highest sensitivity, low cost, and
ease of deployment. Transition metal oxides especially the NixMnxOx are widely used for thermistor synthesis
in spite of the main difficulty of predicting the final sensor characteristics before the actual synthesis.
In view of the above, we report an Artificial Neural Network (ANN) technique to accomplish the synthesis
with predictable results saving valuable resources. In the said ANN modeling we use hyperbolic
tangent sigmoid transfer function for input layer and linear transfer function for the output layer. Levenberg-Marquardt feed-forward algorithm trains the neural net. We measure the performance of the ANN
model with regard to mean square error (MSE) and the correlation coefficient between expected output and
output provided by the network. Moreover, we uniquely model the resistance-temperature (R-T) characteristics
of different thermistor samples using optimized ANN structure. To model such sort of behavior, we
provide nickel content, room temperature resistance, and concentration of oxalic acid as an input data to
the network and predict the nickel acetate and manganese acetate concentration. The accomplished ANN
modeling evidences a lower number of hidden neuron architecture exhibiting optimum performance as regards
to prediction accuracy. The lower number of hidden neurons signifies a lesser amount of memory required
for prediction of different chemical composition. Thus, we demonstrate exploitation of modeling,
simulation and soft computational approaches for predicting the best suitable chemical composition and
thus establish the synergy between the materials science and soft computing paradigm
