Kristin Persson2 1

2, Materials Science and Engineering, University of California, Berkeley, Berkeley, California, United States
1, Lawrence Berkeley National Laboratory, Berkeley, California, United States

The tremendous improvements in computational resources, coupled with software development during the last decades, real materials properties can now be calculated from quantum mechanics – much faster than they can be measured. A result of this paradigm change are databases like the Materials Project ( which is harnessing the power of supercomputing together with state of the art quantum mechanical theory to compute the properties of all known inorganic materials and beyond, design novel materials and offer the data for free to the community together with online analysis and design algorithms. The software infrastructure carries out thousands of calculations per week – enabling screening, predictions, characterization and even synthesis suggestions - for both novel solid as well as molecular species with target properties. This growing body of data has finally reached the stage where automated learning algorithms can be effectively trained and utilized to accelerate analyses. To exemplify the approach of data-driven materials design, we will survey a few case studies – from prediction, to synthesis and characterization - showcasing rapid iteration between ideas, computations, insight and new materials development.