All bespoke programs presented here are implemented in Python. The MOF porosity prediction and thermal conductivity tools use random forests implemented in sci-kit learn . MOF porosity prediction tool was featurised using mordred and RDKit . The Li-Ion conductivity tool uses CrabNet to predict the conductivities of compositions. ElMTree uses the ElMD 9 library and a simple implementation of the list of clusters. ElM2D uses the aforementioned ElMTree application with the UMAP and plotly libraries. The modeling of heat capacity was carried forward using the SciPy library . Each of the web applications are implemented using Flask, with the gunicorn process manager used to spawn Flask processes, examples of these implementations have been provided . VMs are run through VMWare vSphere ESXi 7 , with Nginx used to serve HTTP responses to users and route URLs to each VM . Example setup scripts for gunicorn and Nginx can are available . The linux utility crontab is used to to ensure regular updates are executed and to remove temporary files in relevant LMDS applications.