Machine learning as an improved estimator for magnetization curve and spin gap

Research output: Contribution to journalArticlepeer-review

Abstract

By applying a machine learning algorithm to extrapolations and the numerical differentiations, we propose a method to obtain a continuous magnetization curve out of discrete energy data. It gives an expression for the spin gap, which converges faster to the thermodynamic limit. We check its validity for an exactly solvable one-dimensional spin model and apply it to the kagome antiferromagnet. Results of the kagome antiferromagnet obtained by the exact-diagonalization data up to 30 sites were comparable to the DMRG results for the 132 sites.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2019 Feb 8

ASJC Scopus subject areas

  • General

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