Abstract

Eigenvalue problems and linear systems of equations involving large symmetric matrices are commonly solved in quantum chemistry using Krylov space methods, such as the Davidson algorithm. The preconditioner is a key component of Krylov space methods that accelerates convergence by improving the quality of new guesses at each iteration. We systematically design a new preconditioner for time-dependent density functional theory (TDDFT) calculations based on the recently introduced TDDFT-ris semiempirical model by retuning the empirical scaling factor and the angular momenta of a minimal auxiliary basis. The final preconditioner produced includes up to d-functions in the auxiliary basis and is named "rid". The rid preconditioner converges excitation energies and polarizabilities in 5-6 iterations on average, a factor of 2-3 faster than the conventional diagonal preconditioner, without changing the converged results. Thus, the rid preconditioner is a broadly applicable and efficient preconditioner for TDDFT calculations.

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