Forecasting nonadiabatic dynamics using hybrid convolutional neural network/long short-term memory network-Reference-Cited by-同舟云学术

Forecasting nonadiabatic dynamics using hybrid convolutional neural network/long short-term memory network

Published:2021-12-14 Issue:22 Volume:155 Page:224104
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Wu Daxin¹²,Hu Zhubin¹³^ORCID,Li Jiebo²,Sun Xiang¹³^ORCID

Affiliation:

1. Division of Arts and Sciences, NYU Shanghai, 1555 Century Avenue, Shanghai 200122, China

2. Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Institute of Medical Photonics, Beihang University, Beijing 100191, China

3. NYU-ECNU Center for Computational Chemistry at NYU Shanghai, 3663 Zhongshan Road North, Shanghai 200062, China

Funder

National Natural Science Foundation of China

Hefei National Laboratory for Physical Sciences at the Microscale

Shanghai Sailing Program

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0073689

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4. Numerically “exact” approach to open quantum dynamics: The hierarchical equations of motion (HEOM)

5. Path integrals for dissipative systems by tensor multiplication. Condensed phase quantum dynamics for arbitrarily long time

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