Quasi-classical trajectory analysis with isometric feature mapping and locally linear embedding: deep insights into the multichannel reaction on an NH3+(4A) potential energy surface

2020 ◽  
Vol 22 (31) ◽  
pp. 17460-17471 ◽  
Author(s):  
Weiliang Shi ◽  
Tian Jia ◽  
Anyang Li
Keyword(s):  
Potential Energy ◽  
Trajectory Analysis ◽  
Energy Surface ◽  
Classical Trajectory ◽  
Locally Linear Embedding ◽  
Feature Mapping ◽  
Classical Trajectories ◽  
Linear Embedding ◽  
Isometric Feature Mapping ◽  
Locally Linear

Two manifold learning methods, isometric feature mapping and locally linear embedding, are applied to the analysis of quasi-classical trajectories for multi-channel reaction NH+ + H2 → N + H3+/NH2+ + H.

scholarly journals Comparación de métodos de reducción de dimensión basados en análisis por localidades

TecnoLógicas ◽  
2010 ◽  
pp. 131
Author(s):  
Juliana Valencia-Aguirre ◽  
Genaro Daza-Santacoloma ◽  
Carlos D. Acosta ◽  
Germán Castellanos-Domínguez
Keyword(s):  
Locally Linear Embedding ◽  
Feature Mapping ◽  
Maximum Variance ◽  
Linear Embedding ◽  
Isometric Feature Mapping ◽  
Maximum Variance Unfolding ◽  
Locally Linear

En este trabajo se realiza una comparación de las principales técnicas de reducción de dimensión no lineal basadas en análisis por localidades, tales como: Locally linear embedding, Isometric feature mapping y Maximum variance unfolding. El estudio pretende determinar, bajo criterios objetivos, cuál de las técnicas consideradas conserva de mejor manera las propiedades locales de la variedad, y la estructura global de los datos de entrada al realizar un mapeo a un espacio de menor dimensión. Los métodos son especialmente analizados en aplicaciones de visualización. Las inmersiones obtenidas son evaluadas por medio de dos criterios: Error de Conservación de Vecindarios y Promedio de Vecinos Conservados. Para la validación experimental se utilizan bases de datos artificiales y reales que permiten confirmar visualmente la calidad de las inmersiones obtenidas. Con base en los resultados se observa que la técnica Maximum variance unfolding presenta inmersiones de mejor calidad, debido a que la técnica de optimización de este algoritmo preserva exactamente las distancias entre puntos cercanos en el espacio de baja dimensión, conservando la estructura global de la variedad analizada.

Locally Linear Embedding Based on Relative Manifold

2009 ◽  
Vol 20 (9) ◽  
pp. 2376-2386 ◽  
Author(s):  
Gui-Hua WEN ◽  
Ting-Hui LU ◽  
Li-Jun JIANG ◽  
Jun WEN
Keyword(s):  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

Bearing feature extraction using multi-structure locally linear embedding

2021 ◽  
Vol 428 ◽  
pp. 280-290
Author(s):  
Yuanhong Liu ◽  
Zebiao Hu ◽  
Yansheng Zhang
Keyword(s):  
Feature Extraction ◽  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

A quasi-classical trajectory study of the reagent initial rotation quantum state effect on the reaction He + T2+ → HeT+ + T using an improved ab initio potential energy surface

2012 ◽  
Vol 90 (2) ◽  
pp. 230-236 ◽  
Author(s):  
Ningjiu Zhao ◽  
Yufang Liu
Keyword(s):  
Angular Momentum ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Quantum Number ◽  
Relative Velocity ◽  
Energy Surface ◽  
Rotational Quantum Number ◽  
Chem Phys ◽  
Classical Trajectory ◽  
Positive Direction

In this work, we employed the quasi-classical trajectory (QCT) method to study the vector correlations and the influence of the reagent initial rotational quantum number j for the reaction He + T2+ (v = 0, j = 0–3) → HeT+ + T on a new potential energy surface (PES). The PES was improved by Aquilanti co-workers (Chem. Phys. Lett. 2009. 469: 26–30). The polarization-dependent differential cross sections (PDDCSs) and the distributions of P(θr), P([Formula: see text]r), and P(θr, [Formula: see text]r) are presented in this work. The plots of the PDDCSs provide us with abundant information about the distribution of the product angular momentum polarization. The P(θr) is used to describe the correlation between k (the relative velocity of the reagent) and j′ (the product rotational angular momentum). The distribution of dihedral angle P([Formula: see text]r) shows the k–k′–j′ (k′ refers to the relative velocity of the product) correlation. The PDDCS calculations illustrate that the product of this reaction is mainly backward scatter and it has the strongest polarization in the backward and sideways scattering directions. At the same time, the results of the P([Formula: see text]r) demonstrate that the product HeT+ tends to be oriented along the positive direction of the y axis and it tends to rotate right-handedly in planes parallel to the scattering plane. Moreover, the distribution of the P(θr) manifests that the product angular momentum is aligned along different directions relative to k. The direction of the product alignment may be perpendicular, opposite, or parallel to k. Moreover, our calculations are independent of the initial rotational quantum number.

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