Model Order Reduction for Quantum Molecular Dynamics

Molecular dynamics simulations are indispensable for exploring the behavior of atoms and molecules. Grounded in quantum mechanical principles, quantum molecular dynamics provides high predictive power but its computational cost is dominated by iterative high-fidelity electronic structure calculations. We propose a novel model order reduction approach as an alternative to high-fidelity electronic structure calculation. By learning a low-dimensional representation of the electronic solution manifold within the Kohn-Sham density functional theory framework, our model order reduction approach determines the ground state electronic density by projecting the problem onto a low-dimensional subspace, thereby avoiding the computationally expensive iterative optimization of electronic wavefunctions in the full space. We demonstrate the capability of our method on a water molecule, showing excellent agreement with high-fidelity simulations for both molecular geometry and dynamic properties, highlighting the generalizability through carefully designed parametrization and systematic sampling.