Machine learning topological phases in real space

7 Jan 2019N. L. HolandaM. A. R. Griffith

We develop a supervised machine learning algorithm that is able to learn topological phases of finite condensed matter systems from bulk data in real lattice space. The algorithm employs diagonalization in real space together with any supervised learning algorithm to learn topological phases through an eigenvector ensembling procedure... (read more)

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