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Molecular dynamics (MD) simulations with full-dimensional potential energy surfaces (PESs) obtained from high-level ab initio calculations are frequently used to model reaction dynamics of small molecules (i.e., molecules with up to 10 atoms). Construction of full-dimensional PESs for larger molecules is, however, not feasible since the number of ab initio calculations required grows rapidly with the increase of dimension. Only a small number of coordinates are often essential for describing the reactivity of even very large systems, and reduced-dimensional PESs with these coordinates can be built for reaction dynamics studies. While analytical methods based on transition-state theory framework are well established for analyzing the reduced-dimensionalPESs, MD simulation algorithms capable of generating trajectories on such surfaces are more rare. In this work, we present a new MD implementation that utilizes the relaxed reduced-dimensional PES for standard micro canonical (NVE) and canonical (NVT) MD simulations.The method is applied to the pyramidal inversion of a NH3molecule. The results from the MD simulations on a reduced, three-dimensional PES are validated against the ab initio MD simulations, as well as MD simulations on full-dimensional PES and experimental data.
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Nuclear Norm Based Spectrum Estimation for Molecular Dynamic Simulations
Molecular dynamic (MD) simulations are used to probe molecular systems in regimes not accessible to physical experiments. A common goal of these simulations is to compute the power spectral density (PSD) of some component of the system such as particle velocity. In certain MD simulations, only a few time locations are observed, which makes it difficult to estimate the autocorrelation and PSD. This work develops a novel nuclear norm minimization-based method for this type of sub-sampled data, based on a parametric representation of the PSD as the sum of Lorentzians. We show results on both synthetic data and a test system of methanol.
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- PAR ID:
- 10253917
- Date Published:
- Journal Name:
- The 54th Asilomar Conference on Signals, Systems, and Computers
- Page Range / eLocation ID:
- 1457 to 1461
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IEEECONF51394.2020.9443532
