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Published on: April 19, 2018
Extrapolating Quantum Observables with Machine Learning: Inferring Multiple Phase Transitions from Properties of a
Rodrigo A Vargas-Hernández1, John Sous1,2,3, Mona Berciu2,3
1Department of Chemistry, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z1.
Abstract:
We present a machine-learning method for predicting sharp transitions in a Hamiltonian phase diagram by extrapolating the properties of quantum systems. The method is based on Gaussian process regression with a combination of kernels chosen through an iterative procedure maximizing the predicting power of the kernels. The method is capable of extrapolating across the transition lines. The calculations within a given phase can be used to predict not only the closest sharp transition but also a transition removed from the available data by a separate phase. This makes the present method particularly valuable for searching phase transitions in the parts of the parameter space that cannot be probed experimentally or theoretically.
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