Our automation and cloud-computing services are connected to an SQL database that stores and manages your data and that of the community. This stored knowledge creates increased efficiency by providing community access to already computed structures and energies.
With an increasing database size we will also be continuously analyzing the performance of different levels of theory against experimental and computed data using machine learning techniques. Ultimately this will allow us to predict the appropriate level of theory for a given question, whether it be accurate structures, energies (ground and transition-state) spectroscopic transition energies, etc.