Supervise Machine Learning Interface to Predict Physical Properties of Binary Solutions

The progress in biological and chemical research provides valuable physiological and chemical functionalities using the basic physical properties of life systems. The major among them is Density, Surface tension, Viscosity etc. However, it is not possible to evaluate the precise and accurate values of these properties for the desired concentration. The evolution of supervised machine learning algorithm can provide the predicted values of physical properties, provided the accurate and highly precise experimental values are available. Present study is an attempt to evolve the Supervise machine learning trained network to predict the physical properties of binary solution with selected variables and constrains. An attempt is also made to design the generalized algorithm and method to evaluate any property of binary solution.