Applying the Monte Carlo Method to Problems of Modeling Stationary States of Quantum Systems by Means of Machine Learning

An overview is presented of recent advances in applying variational, projection, and diffusion Monte Carlo methods (VMC, PMC, and DMC, respectively). The prospects for using the Monte Carlo method to calculate path integrals (PIMC) when modeling molecular systems are also considered. It is emphasized that modern machine learning effectively meets the requirements of parameterizing wave functions of the target space of solutions for a wide range of problems in quantum molecular modeling and functionals of the corresponding observables.