Industry has developed along with historical finding and dramatic improvement of functional materials. The typical examples can be found for automobile in exhaust gas purification catalysts, Li-ion batteries, magnet motors, and fuel cells. On the other hand, research and development of materials may take huge resources and long time. In order to accelerate to develop the materials on demand, we have developed machine learning algorithm with DFT accuracy that can access to high-throughput simulations for practical size of materials modelling. DFT data sets for simple models are stored in a database, which is used to predict energy and force in a practical model through similarity kernels, such as Gaussian or polynomial function of power spectrum.1, 2 The regression coefficients are determined to reproduce the DFT training data by using a Bayesian linear regression method. Applications to catalytic activity of nanoparticles3 and diffusion properties in solid-state ionic conductor2 demonstrate that the present data-driven method is promising to predict chemical reactions and transport properties, which are not easily determined only with DFT calculations, thus to design a variety of functional materials.
 Bartok, Kondor Csanyi, Phys. Rev. B 87, 184115 (2013).
 Miwa, Ohno, Phys. Rev. Mater. 1, 053801 (2017); Phys. Rev. B 94, 184109 (2016).
 Jinnouchi, Asahi, J. Phys. Chem. Lett. 8, 4279 (2017).