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We present SCALINN -- Strongly Correlated Approach with Language InspiredNeural Network -- as a method for solving the Anderson impurity model andreducing the computational cost of dynamical mean-field theory calculations.Inspired by the success of generative Transformer networks in natural languageprocessing, SCALINN utilizes an in-house modified Transformer network in orderto learn correlated Matsubara Green's functions, which act as solutions to theimpurity model. This is achieved by providing the network with low-costMatsubara Green's functions, thereby overcoming the computational cost of highaccuracy solutions. Across different temperatures and interaction strengths,the performance of SCALINN is demonstrated in both physical observables(spectral function, Matsubara Green's functions, quasi-particle weight), andthe mean squared error cost values of the neural network, showcasing thenetwork's ability to accelerate Green's function based calculations ofcorrelated materials.

A language-inspired machine learning approach for solving strongly correlated problems with dynamical mean-field theory