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arXiv 2204.05967cond-mat.str-elcond-mat.dis-nncs.LG
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Machine learning predictions for local electronic properties of disordered correlated electron systems

National Tsing Hua University(国立清华大学);Academia Sinica(中央研究院);University of Virginia(弗吉尼亚大学);National Sun Yat-sen University(国立中山大学)

Yi-Hsuan Liu, Sheng Zhang, Puhan Zhang, Ting-Kuo Lee, Gia-Wei Chern

英文摘要:

We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron systems. Our approach is based on the locality principle, or the nearsightedness nature, of many-electron systems, which means local electronic properties depend mainly on the immediate environment. A ML model is developed to encode this complex dependence of local quantities on the neighborhood. We demonstrate our approach using the square-lattice Anderson-Hubbard model, which is a paradigmatic system for studying the interplay between Mott transition and Anderson localization. We develop a lattice descriptor based on group-theoretical method to represent the on-site random potentials within a finite region. The resultant feature variables are used as input to a multi-layer fully connected neural network, which is trained from datasets of variational Monte Carlo (VMC) simulations on small systems. We show that the ML predictions agree reasonably well with the VMC data. Our work underscores the promising potential of ML methods for multi-scale modeling of correlated electron systems.

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