Materials simulations are dominated by either quantum mechanics (QM) based methods—which are time-intensive, but accurate and versatile—or semi-empirical/ classical methods—which are fast but are significantly limited in veracity, versatility and transferability. Machine learning (ML) methods have the potential to bridge the chasm between the two extremes and can combine the best of both worlds. We have created a ML platform, trained on accurate QM reference data, for the rapid prediction of properties such as potential energy, atomic forces, stresses, charge density, and the electronic density of states, at a minuscule fraction of the QM cost. The ML models can also be progressively improved in quality by periodically (or on-demand) exposing them to fresh QM data in regions of poor performance. Here, we demonstrate the power and versatility of this new platform in correctly capturing electronic, thermodynamic, mechanical, and diffusive properties for a variety of systems.