Quantum molecular similarity. Part 2: The relation between properties in BCP space and bond length

1999 ◽  
Vol 77 (1) ◽  
pp. 28-36 ◽  
Author(s):  
Sean E O'Brien ◽  
Paul LA Popelier
Keyword(s):  
Bond Length ◽  
Critical Points ◽  
Molecular Similarity ◽  
Bond Critical Point ◽  
Abstract Space ◽  
Data Set ◽  
Equilibrium Bond Length ◽  
Linear Relationships ◽  
Quantum Molecular Similarity ◽  
Picture Show

In previous work we introduced an abstract space of bond critical point properties, called BCP space, to measure the similarity between molecules. In this contribution we critically examine the dependence of properties in BCP space on equilibrium bond length, a topic which has been extensively investigated in the literature. For that purpose we have designed a data set of 57 molecules yielding 731 bond critical points. We confirm the existence of local linear relationships provided the bonds vary little in their chemical surroundings. Such relationships break down completely for larger subsets of bond critical points encompassing a wider variety of bonds. The patterns observed in the global picture show so little correlation that one may safely conclude that BCP properties cannot be trivially recovered or even predicted by knowledge of bond length alone.Key words: atoms in molecules, molecular similarity, bond critical points.

Study of Local Reactivity of Isatoic Anhydride Applying Quantum Molecular Similarity

2021 ◽  
Vol 6 (17) ◽  
pp. 4390-4399
Author(s):  
Tomás Guerrero ◽  
Sergio F. Juárez‐Cerrillo ◽  
Myrna H. Matus ◽  
José Sergio Durand‐Niconoff
Keyword(s):  
Molecular Similarity ◽  
Isatoic Anhydride ◽  
Local Reactivity ◽  
Quantum Molecular Similarity

scholarly journals Robust Support Vector Regression Model in the Presence of Outliers and Leverage Points

2017 ◽  
Vol 11 (8) ◽  
pp. 92
Author(s):  
Waleed Dhhan ◽  
Habshah Midi ◽  
Thaera Alameer
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Data Set ◽  
Support Vector Regression Model ◽  
Leverage Points ◽  
Linear Relationships ◽  
Linear And Nonlinear ◽  
Abnormal Points ◽  
Robust Support Vector Regression

Support vector regression is used to evaluate the linear and non-linear relationships among variables. Although it is non-parametric technique, it is still affected by outliers, because the possibility to select them as support vectors. In this article, we proposed a robust support vector regression for linear and nonlinear target functions. In order to carry out this goal, the support vector regression model with fixed parameters is used to detect and minimize the effects of abnormal points in the data set. The efficiency of the proposed method is investigated by using real and simulation examples.

REFINEMENT OF THE ${\rm{Si}}(111) \mbox{-} (\surd{3} \times \surd{3}){\rm{R30}}^\circ - {\rm{Ag}}$ STRUCTURE BY LOW-ENERGY ELECTRON DIFFRACTION

1995 ◽  
Vol 02 (04) ◽  
pp. 451-457 ◽  
Author(s):  
H. OVER ◽  
S.Y. TONG ◽  
J. QUINN ◽  
F. JONA
Keyword(s):  
Experimental Data ◽  
Electron Diffraction ◽  
Bond Length ◽  
Debye Temperature ◽  
Low Energy ◽  
Energy Electron ◽  
Data Set ◽  
Low Energy Electron Diffraction ◽  
The Third ◽  
Low Energy Electron

We have reinvestigated the bond geometry of the [Formula: see text] surface by means of low-energy electron diffraction using a much larger experimental data set than that previously used. The [Formula: see text] surface consists of a [Formula: see text] lattice of Ag atoms which replaces the topmost Si atoms, and forces the remaining Si atoms to form trimers. The Ag-Ag bond length turned out to be 3.47±0.12 Å. The Ag atoms are laterally displaced from the bulk positions of the Si atoms which they have replaced by 0.53 Å resulting in a Ag-Si bond length of 2.36±0.17 Å. The missing top Si layer and the formation of Si trimers lead to strong distortions in deeper Si layers, most notably a buckling in the third and fourth Si layer with a magnitude of about 0.35 Å and 0.2 Å, respectively. Applying the concept of ‘split positions’, the low Debye temperature of Ag has been interpreted as being caused by strong in-plane (either static or dynamic) movements of the Ag atoms perpendicular to the Ag-Si bonding.

scholarly journals NMR Determination of Amide N−H Equilibrium Bond Length from Concerted Dipolar Coupling Measurements

2008 ◽  
Vol 130 (49) ◽  
pp. 16518-16520 ◽  
Author(s):  
Lishan Yao ◽  
Beat Vögeli ◽  
Jinfa Ying ◽  
Ad Bax
Keyword(s):  
Bond Length ◽  
Dipolar Coupling ◽  
Equilibrium Bond Length

A comparison of the different versions of popular technology acceptance models

Kybernetes ◽  
2015 ◽  
Vol 44 (5) ◽  
pp. 788-805 ◽  
Author(s):  
Francisco Javier Rondan-Cataluña ◽  
Jorge Arenas-Gaitán ◽  
Patricio Esteban Ramírez-Correa
Keyword(s):  
Technology Acceptance ◽  
Linear Model ◽  
Mobile Internet ◽  
Data Set ◽  
Content Type ◽  
Use Of Technology ◽  
Internet Users ◽  
Linear Relationships ◽  
Technology Acceptance Models ◽  
Non Linear

Purpose – The purpose of this paper is to provide a complete and chronological view of the evolution of the main acceptance and use of technology models, from the 1970s to the present day. Design/methodology/approach – A comparison of partial least squares (linear model) and WarpPLS (non-linear model) has been run for each acceptation of technology model: TRA, TAM0, TAM1, TAM2, TAM3, UTAUT, UTAUT2. The data set collects the information of mobile internet users. Findings – The authors have concluded that UTAUT2 model obtains a better explanation power than the rest of technology acceptance models (TAMs) in the sample of mobile internet users. Furthermore, all models have a better explanation power using non-linear relationships than the traditional linear approach. Originality/value – The vast majority of research published to date with regard to the Theory of Reasoned Action (TRA), the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT) are based on structural equation models assuming linear relationships between variables. The originality of this study is that it incorporates non-linear relationships and compares the same models using both approaches.

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