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Quantum dynamics using path integral coarse-graining
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AI
DOI：10.1063/5.0120386.png)
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摘要
En 中文
The vibrational spectra of condensed and gas-phase systems are influenced by thequantum-mechanical behavior of light nuclei. Full-dimensional simulations of approximate quantum dynamics are possible thanks to the imaginary time path-integral (PI) formulation of quantum statistical mechanics, albeit at a high computational cost which increases sharply with decreasing temperature. By leveraging advances in machine-learned coarse-graining, we develop a PI method with the reduced computational cost of a classical simulation. We also propose a simple temperature elevation scheme to significantly attenuate the artifacts of standard PI approaches as well as eliminate the unfavorable temperature scaling of the computational cost. We illustrate the approach, by calculating vibrational spectra using standard models of water molecules and bulk water, demonstrating significant computational savings and dramatically improved accuracy compared to more expensive reference approaches. Our simple, efficient, and accurate method has prospects for routine calculations of vibrational spectra for a wide range of molecular systems - with an explicit treatment of the quantum nature of nuclei.
Keyword:
STATISTICAL-MECHANICS
LIQUID WATER
NUCLEAR
SPECTROSCOPY
SPECTRUM
DIPOLE
POINT
期刊
IF:
3.1
论文数:
7.2W
被引数:
23.2W
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引用论文
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Mean-field Matsubara dynamics: Analysis of path-integral curvature effects in rovibrational spectra
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