47 citations · 104 across the 5 of their papers we have counts for
5 papers
Machine learning the dynamics of quantum kicked rotor
Tomohiro Mano, Tomi Ohtsuki
Using the multilayer convolutional neural network (CNN), we can detect the quantum phases in random electron systems, and phase diagrams of two and higher dimensional Anderson tran…
Analysis of Kohn-Sham Eigenfunctions Using a Convolutional Neural Network in Simulations of the Metal-insulator Transition in Doped Semiconductors
Yosuke Harashima, Tomohiro Mano, Keith Slevin +1
Machine learning has recently been applied to many problems in condensed matter physics. A common point of many proposals is to save computational cost by training the machine with…
Application of Convolutional Neural Network to Quantum Percolation in Topological Insulators
Tomohiro Mano, Tomi Ohtsuki
Quantum material phases such as the Anderson insulator, diffusive metal, and Weyl/Dirac semimetal as well as topological insulators show specific wave functions both in real and Fo…
Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions
Tomi Ohtsuki, Tomohiro Mano
Applications of neural networks to condensed matter physics are becoming popular and beginning to be well accepted. Obtaining and representing the ground and excited state wave fun…
Phase Diagrams of Three-Dimensional Anderson and Quantum Percolation Models using Deep Three-Dimensional Convolutional Neural Network
Tomohiro Mano, Tomi Ohtsuki
The three-dimensional Anderson model is a well-studied model of disordered electron systems that shows the delocalization--localization transition. As in our previous papers on two…