We are developing TINC, a new toolkit for interactive computation and visualization, applying it to materials simulations approaches, interfacing this toolkit with the software package CASM, designed to enable first-principles statistical mechanics studies of complex crystalline materials through a custom application built on top of TINC. TINC is being developed for a fundamental study of ion transport and intercalation processes in the electrode materials of sodium (Na) ion batteries. TINC ties computation and display to data through user interaction, a general-purpose platform useful in different domains. Researchers performed rapid iterations of Grand Canonical and Kinetic Monte Carlo simulations generated on the HPC cluster, facilitating verification of simulations in a near-to-real-time environment, a faster path to insight and validation. TINC can analyze data generated with Bayesian learning tools such as STAN. Bayesian regression is used in all disciplines of engineering.