Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning

The use of machine
learning (ML) to refine low-level theoretical
calculations to achieve higher accuracy is a promising and actively
evolving approach known as Δ-ML. The density matrix renormalization
group (DMRG) is a powerful variational approach widely used for studying
strongly correlated quantum systems. High computational efficiency
can be achieved without compromising accuracy. Here, we demonstrate
the potential of a simple ML model to significantly enhance the performance
of the quantum chemical DMRG method.