Understanding electron transport in materials and across their interface is very important to device applications such as energy conversion, electronics, and spintronics. However, relevant physical and chemical phenomena are hardly predictable from our intuition relying on rather classical theories. Therefore, with learning modern quantum theory of matter, theoretical simulation is as important as experiments to get important insight thus design innovative materials. One of our research interests is to predict various properties of materials by using the state-of-the-art simulation tools. For efficient prediction, we carry out high-throughput calculations by the density functional theory packages with machine learning assisted screening. Nevertheless, it has limitation in predicting the correlation effect related phenomena of various complex system. To overcome limitations of existing codes, we also develop advanced software tools with the dynamical mean field theory and non-equilibrium Green’s function methods.