Ab initio molecular dynamics with nuclear quantum effects at classical cost: ring polymer contraction for density functional theory
Ondrej Marsalek, Thomas E. Markland

TL;DR
This paper introduces an efficient ab initio molecular dynamics method incorporating nuclear quantum effects at classical computational cost by extending ring polymer contraction techniques, enabling accurate simulations of light nuclei systems.
Contribution
The authors develop the AI-RPC scheme that reduces the cost of path integral simulations, making nuclear quantum effects accessible in ab initio molecular dynamics at negligible extra cost.
Findings
AI-RPC achieves rapid convergence to full path integral results.
The method captures most nuclear quantum effects using only the ring polymer centroid.
Simulation speed is comparable to classical ab initio molecular dynamics, 35 times faster than full path integral methods.
Abstract
Path integral molecular dynamics simulations, combined with an ab initio evaluation of interactions using electronic structure theory, incorporate the quantum mechanical nature of both the electrons and nuclei, which are essential to accurately describe systems containing light nuclei. However, path integral simulations have traditionally required a computational cost around two orders of magnitude greater than treating the nuclei classically, making them prohibitively costly for most applications. Here we show that the cost of path integral simulations can be dramatically reduced by extending our ring polymer contraction approach to ab initio molecular dynamics simulations. By using density functional tight binding as a reference system, we show that our ab initio ring polymer contraction (AI-RPC) scheme gives rapid and systematic convergence to the full path integral density…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
