The manufacturing parameters chosen by the users are handled and evaluated in terms of the processability of the components used, and feedback is provided during the electrochemical testing step on whether any of the manufacturing parameter values adopted during the manufacturing process do not fall within the expected range. Moreover, for the calculation of final electrode and cell properties, simple mechanistic equations that capture the effects of key parameters are used, which provide the main trends of the particular process. In the future, more complex models can be directly incorporated into the application to evaluate the electrode properties, accounting for the effects of all the parameters. To elaborate further, in general, a process such as coating and drying begins with an initial component (e.g., slurry) (Figure ). The process parameters (e.g., line speed, drying rate) are then chosen based on the input component's properties (e.g., viscosity, solid content) and the process history (e.g., storage time of slurry ), which leads to an operation on the component, thus producing a processed component, such as a dried electrode. This process A (cf. Figure ) can be modelled using mechanistic equations, as demonstrated in this Concept, along with physics-based modelling , and/or with deep learning models . Moreover, transfer learning could be used to apply these models to the different chemistries present in the BMM environment . The BMM features a configuration panel accessible via a browser, allowing instructors to manage virtual rooms, save/load states, and provide additional instructions and tips through a GUI. This interface supports unlimited student practice by saving the state of all actions and objects, enables instructors to prepare the session beforehand, modify notes while instructing to better suit the individual student's capabilities, set up the room to study specific processes, and pause and continue sessions seamlessly.