The development of density-functional theory in the 1960s and the dissemination of computers led to a revolution in materials science. A third kind of physics, computational physics, emerged to complement its theoretical and experimental sisters. Nowadays, with the availability of ever faster supercomputers and novel computer methodologies, we are living what one can call the second computer revolution in materials science. High throughput techniques, together with ever faster supercomputers, allow for the automatic screening of thousands or even millions of hypothetical materials to find solutions to present technological challenges. Moreover, machine learning methods are used to accelerate materials discovery by complementing density-functional theory with extremely efficient statistical models. In this talk we summarize our recent attempts to discover, characterize, and understand inorganic compounds using these novel approaches. We start by motivating why the search for new materials is nowadays one of the most pressing technological problems. Then we summarize our recent work in using crystal-graph attention neural networks for the prediction of materials properties. To train these networks, we curated a dataset of over 2 million density-functional calculations with consistent calculation parameters. Combining the data and the newly developed networks we have already scanned more than two thousand prototypes spanning a space of more than one billion materials and identified tens of thousands of theoretically stable compounds. We then discuss how simple, interpretable machine learning approaches can be used to understand complex material properties, such as the transition temperature of superconductors. Finally, we speculate which role machine learning will have in the future of materials science.
 Recent advances and applications of machine learning in solid-state materials science, J. Schmidt, M.R.G. Marques, S. Botti, and M.A.L. Marques, NPJ Comput. Mater. 5, 83 (2019).
 Crystal-graph attention networks for the prediction of stable materials, J. Schmidt, L. Pettersson, C. Verdozzi, S. Botti, and M.A.L. Marques, Sci. Adv. 7, eabi7948 (2021).
Paulo Brás, Paulo Silva, Jaime Silva