Artificial intelligence and the competitiveness of perturbative approaches

Due to their many past shortcomings perturbation techniques have been seldom used in theoretical chemistry. However, the introduction of operator- and superoperator-based techniques has represented a real breakthrough in their usefulness as a powerful tool of calculation. We analyze here how an artificial intelligence (AI) approach to their automation can save the obstacle of their cumbersome algebra and, by manipulating full analytic expressions with the help of a simple combinatorial analysis, represents an attractive alternative to more orthodox, purely numerical, approaches.