Abstract
The project “Multiscale Investigation Of Switchable Metallicity In Tunable Conductance Devices” en- compasses a comprehensive web application designed to revolutionize the way businesses handle MnS2 components, such as pressure switches, variable resistors, memory devices, batteries, and supercapacitors, through a series of interconnected modules. At its core, this application streamlines client interactions, optimizes MnS2 material processing, and ensures seamless application integration, all while leveraging advanced porosity analysis techniques. A significant enhancement in the Porosity Analysis module is the incorporation of the Decision Tree Regressor, a machine learning algorithm, to accurately evaluate the conductivity of materials. This addition not only boosts the precision of porosity and conductivity assessments but also empowers the system to predict material performance with higher accuracy, thereby enabling more tailored solutions for client requirements.
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