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.
|Publication status||Published - 2019 Feb 8|
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