Computing excited states of molecules using normalizing flows
This work addresses a domain-specific problem for computational chemists by providing an incremental improvement in efficiency for computing vibrational spectra of high-dimensional molecular systems.
The paper tackles the computational challenge of calculating highly excited and delocalized molecular vibrational states by introducing a method that uses normalizing flows to learn optimal vibrational coordinates, resulting in significantly increased accuracy and enhanced basis-set convergence for the 100 lowest excited states of molecules like H2S, H2CO, and HCN/HNC.
Calculations of highly excited and delocalized molecular vibrational states are computationally challenging tasks, which strongly depends on the choice of coordinates for describing vibrational motions. We introduce a new method that leverages normalizing flows -- parametrized invertible functions -- to learn optimal vibrational coordinates that satisfy the variational principle. This approach produces coordinates tailored to the vibrational problem at hand, significantly increasing the accuracy and enhancing basis-set convergence of the calculated energy spectrum. The efficiency of the method is demonstrated in calculations of the 100 lowest excited vibrational states of H$_2$S, H$_2$CO, and HCN/HNC. The method effectively captures the essential vibrational behavior of molecules by enhancing the separability of the Hamiltonian and hence allows for an effective assignment of approximate quantum numbers. We demonstrate that the optimized coordinates are transferable across different levels of basis-set truncation, enabling a cost-efficient protocol for computing vibrational spectra of high-dimensional systems.