Converging TDDFT calculations in 5 iterations with minimal auxiliary preconditioning
arxiv(2024)
摘要
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 re-tuning 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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要