An (artificial) neural network is one of the machine learning methods that imitate the neural structure of the animal brain. A neural network has a structure in which many nodes (neurons) are connected. There are various types of neural networks. Typical examples are feed-forward neural networks (also called perceptrons) used for supervised learning and restricted Boltzmann machines used for unsupervised learning. In recent years, it has become possible to dramatically improve the learning ability by introducing a structure composed of many layers (deep neural networks). Neural networks are widely used in various fields such as image recognition, speech recognition, language analysis, model generation, and class classification. Even in the field of materials science, applications to machine learning force fields, variational wave functions, exploration of new materials (materials informatics), etc., are being advanced.
A method for calculating quantum transport properties of a nanostructure coupled to two or more leads under bias. The electron density and conductance of the system under bias can be obtained by calculating the Green’s function of the nanostructure using self energies that account for the effect of the leads.