  We present SCALINN -- Strongly Correlated Approach with Language Inspired
Neural Network -- as a method for solving the Anderson impurity model and
reducing the computational cost of dynamical mean-field theory calculations.
Inspired by the success of generative Transformer networks in natural language
processing, SCALINN utilizes an in-house modified Transformer network in order
to learn correlated Matsubara Green's functions, which act as solutions to the
impurity model. This is achieved by providing the network with low-cost
Matsubara Green's functions, thereby overcoming the computational cost of high
accuracy 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), and
the mean squared error cost values of the neural network, showcasing the
network's ability to accelerate Green's function based calculations of
correlated materials.
