scholarly journals The Effect of the Collective Motion of Pion Cloud on Weak Interactions

1963 ◽  
Vol 30 (3) ◽  
pp. 412b-413
Author(s):  
Takao Okabayashi ◽  
Ikuko Hamamoto ◽  
Akira Hatano ◽  
Toshiko Saikan
Keyword(s):  
Collective Motion ◽  
Weak Interactions ◽  
Pion Cloud

scholarly journals The Effect of the Collective Motion of Pion Cloud on Weak Interactions

1963 ◽  
Vol 29 (5) ◽  
pp. 767-791 ◽  
Author(s):  
Takao Okabayashi ◽  
Ikuko Hamamoto ◽  
Akira Hatano ◽  
Toshiko Saikan
Keyword(s):  
Collective Motion ◽  
Weak Interactions ◽  
Pion Cloud

Two [Ln4] molecular rings folded as compact tetrahedra

2020 ◽  
Vol 49 (21) ◽  
pp. 7182-7188
Author(s):  
Jorge Salinas-Uber ◽  
Leoní A. Barrios ◽  
Olivier Roubeau ◽  
Guillem Aromí
Keyword(s):  
Weak Interactions ◽  
Molecular Rings

A new highly photo-switchable ligand furnishes supramolecular tetrahedral nanomagnets with Ln(iii) ions (Ln = Dy, Tb). Intramolecular weak interactions define the conformation of the ligand, quenching the photochromic activity.

Local interaction rules and collective motion in black neon tetra (Hyphessobrycon herbertaxelrodi) and zebrafish (Danio rerio).

2019 ◽  
Vol 133 (2) ◽  
pp. 143-155 ◽  
Author(s):  
Vicenç Quera ◽  
Elisabet Gimeno ◽  
Francesc S. Beltran ◽  
Ruth Dolado
Keyword(s):  
Danio Rerio ◽  
Collective Motion ◽  
Local Interaction

RELAXATION AND COLLECTIVE MOTION IN SUPERCONDUCTORS. A TWO-FLUID DESCRIPTION

1978 ◽  
Vol 39 (C6) ◽  
pp. C6-488-C6-489 ◽  
Author(s):  
C. J. Pethick ◽  
H. Smith
Keyword(s):  
Collective Motion ◽  
Two Fluid

BETA DECAY OPENS THE WAY TO WEAK INTERACTIONS

1982 ◽  
Vol 43 (C8) ◽  
pp. C8-261-C8-300
Author(s):  
E. Amaldi
Keyword(s):  
Beta Decay ◽  
Weak Interactions ◽  
The Way

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

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