Table · dataset · 2017
Data for Accelerated path-integral simulations using ring-polymer interpolation
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Imaginary-time path-integral (PI) molecular simulations can be used to calculate exact quantum statistical mechanical properties for complex systems containing many interacting atoms and molecules.
Description
The limiting computational factor in a PI simulation is typically the evaluation of the potential energy surface (PES) and forces at each ring-polymer “bead”; for an n-bead ring-polymer, a PI simulation is typically n times greater than the corresponding classical simulation.
To address the increased computational effort of PI simulations, several approaches have been developed recently, most notably based on the idea of ring-polymer contraction which exploits either the separation of the PES into short-range and long-range contributions or the availability of a computationally inexpensive PES which can be incorporated to effectively smooth the ring-polymer PES; neither approach is satisfactory in applications to systems modeled by PESs given by on-the-fly ab initio calculations.
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In this article, we describe a new method, ring-polymer interpolation (RPI), which can be used to accelerate PI simulations without any prior assumptions about the PES. In simulations of liquid water modeled by an empirical PES (or force field) under ambient conditions, where quantum effects are known to play a subtle role in influencing experimental observables such as radial distribution functions, we find that RPI can accurately reproduce the results of fully-converged PI simulations, albeit with far fewer PES evaluations.
This approach therefore opens the possibility of large-scale PI simulations using ab initio PESs evaluated on-the-fly without the drawbacks of current methods.<br><br>Plain text data files used to create Figures 1-5
Links
Where it is published
- DOI doi.org/10.82444/warw.33744949.v1 ↗
DOI / persistent id · from figshare com
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from figshare com
Topics
- From keywords
- Astronomy & Astrophysics · Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Energy · Engineering · Humanities · Life Sciences · Materials Science · Medicine & Health · Ocean & Atmospheric Science · Social Science
- Inferred from text
- Simulation 75% · Text 75% · Theoretical quantum chemistry 77%
Provenance · 1 source records, 21 field assertions
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|---|---|---|---|
| figshare | oai:figshare.com:article/33744949 | 9 d ago | JSON v1 |
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