scholarly journals A Simple Model for Halogen Bond Interaction Energies

Inorganics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 19 ◽  
Author(s):  
Robert Shaw ◽  
J. Hill
Keyword(s):  
Simple Model ◽  
Halogen Bond ◽  
Computational Cost ◽  
Principal Component ◽  
Physical Nature ◽  
Data Sets ◽  
Interaction Energies ◽  
Validation Data ◽  
Equilibrium Separation ◽  
Non Covalent Interactions

Halogen bonds are prevalent in many areas of chemistry, physics, and biology. We present a statistical model for the interaction energies of halogen-bonded systems at equilibrium based on high-accuracy ab initio benchmark calculations for a range of complexes. Remarkably, the resulting model requires only two fitted parameters, X and B—one for each molecule—and optionally the equilibrium separation, R e , between them, taking the simple form E = X B / R e n . For n = 4 , it gives negligible root-mean-squared deviations of 0.14 and 0.28 kcal mol - 1 over separate fitting and validation data sets of 60 and 74 systems, respectively. The simple model is shown to outperform some of the best density functionals for non-covalent interactions, once parameters are available, at essentially zero computational cost. Additionally, we demonstrate how it can be transferred to completely new, much larger complexes and still achieve accuracy within 0.5 kcal mol - 1 . Using a principal component analysis and symmetry-adapted perturbation theory, we further show how the model can be used to predict the physical nature of a halogen bond, providing an efficient way to gain insight into the behavior of halogen-bonded systems. This means that the model can be used to highlight cases where induction or dispersion significantly affect the underlying nature of the interaction.

Non-Covalent Interactions Atlas Benchmark Data Sets 3: Repulsive Contacts

2020 ◽  
Author(s):  
Kristian Kříž ◽  
Martin Nováček ◽  
Jan Řezáč
Keyword(s):  
Organic Molecules ◽  
Energy Surface ◽  
Molecular Complexes ◽  
Data Sets ◽  
Interaction Energies ◽  
Data Set ◽  
Benchmark Data ◽  
Mechanical Methods ◽  
Non Covalent Interactions ◽  
Covalent Interactions

The new R739×5 data set from the Non-Covalent Interactions Atlas series (www.nciatlas.org) focuses on repulsive contacts in molecular complexes, covering organic molecules, sulfur, phosphorus, halogens and noble gases. Information on the repulsive parts of the potential energy surface is crucial for the development of robust empirically parametrized computational methods. We use the new data set of highly accurate CCSD(T)/CBS interaction energies to test existing DFT and semiempirical quantum-mechanical methods. On the example of the PM6 method, we analyze the source of the error and its relation to the difficulties in the description of conformational energies, and we also devise an immediately applicable correction that fixes the most serious uncorrected issues previously encountered in practical calculations.

A simple model for halogen bonds

2018 ◽  
Author(s):  
Robert A. Shaw ◽  
Grant Hill
Keyword(s):  
Simple Model ◽  
Statistical Model ◽  
Halogen Bond ◽  
Coupled Cluster ◽  
Interaction Energies ◽  
Halogen Bonds ◽  
Bond Interaction ◽  
Explicitly Correlated ◽  
Equilibrium Geometries

In this article we develop a simple statistical model for the prediction of halogen bond interaction energies at equilibrium geometries. The model is based on explicitly correlated coupled cluster results and produces root-mean-squared deviations of 0.14 and 0.28 kcal mol<sup>–1</sup> over separate fitting and validation sets, respectively. We also show how the model can be used to highlight cases where induction or dispersion significantly affect the underlying nature of the interaction.<br>

Non-Covalent Interactions Atlas Benchmark Data Sets 3: Repulsive Contacts

2020 ◽  
Author(s):  
Kristian Kříž ◽  
Martin Nováček ◽  
Jan Řezáč
Keyword(s):  
Organic Molecules ◽  
Energy Surface ◽  
Molecular Complexes ◽  
Data Sets ◽  
Interaction Energies ◽  
Data Set ◽  
Benchmark Data ◽  
Mechanical Methods ◽  
Non Covalent Interactions ◽  
Covalent Interactions

The new R739×5 data set from the Non-Covalent Interactions Atlas series (www.nciatlas.org) focuses on repulsive contacts in molecular complexes, covering organic molecules, sulfur, phosphorus, halogens and noble gases. Information on the repulsive parts of the potential energy surface is crucial for the development of robust empirically parametrized computational methods. We use the new data set of highly accurate CCSD(T)/CBS interaction energies to test existing DFT and semiempirical quantum-mechanical methods. On the example of the PM6 method, we analyze the source of the error and its relation to the difficulties in the description of conformational energies, and we also devise an immediately applicable correction that fixes the most serious uncorrected issues previously encountered in practical calculations.

PC 2D-COS: A Principal Component Base Approach to Two-Dimensional Correlation Spectroscopy

2020 ◽  
Vol 74 (4) ◽  
pp. 460-472 ◽  
Author(s):  
Julian Hniopek ◽  
Michael Schmitt ◽  
Jürgen Popp ◽  
Thomas Bocklitz
Keyword(s):  
Computational Cost ◽  
Principal Component ◽  
Correlation Spectroscopy ◽  
High Spectral Resolution ◽  
Data Sets ◽  
Two Dimensional ◽  
Real World Data ◽  
Data Set ◽  
Multiple Data Sets ◽  
2D Correlation Spectroscopy

This paper introduces the newly developed principal component powered two-dimensional (2D) correlation spectroscopy (PC 2D-COS) as an alternative approach to 2D correlation spectroscopy taking advantage of a dimensionality reduction by principal component analysis. It is shown that PC 2D-COS is equivalent to traditional 2D correlation analysis while providing a significant advantage in terms of computational complexity and memory consumption. These features allow for an easy calculation of 2D correlation spectra even for data sets with very high spectral resolution or a parallel analysis of multiple data sets of 2D correlation spectra. Along with this reduction in complexity, PC 2D-COS offers a significant noise rejection property by limiting the set of principal components used for the 2D correlation calculation. As an example for the application of truncated PC 2D-COS a temperature-dependent Raman spectroscopic data set of a fullerene-anthracene adduct is examined. It is demonstrated that a large reduction in computational cost is possible without loss of relevant information, even for complex real world data sets.

A simple model for halogen bonds

2018 ◽  
Author(s):  
Robert A. Shaw ◽  
Grant Hill
Keyword(s):  
Simple Model ◽  
Statistical Model ◽  
Halogen Bond ◽  
Coupled Cluster ◽  
Interaction Energies ◽  
Halogen Bonds ◽  
Bond Interaction ◽  
Explicitly Correlated ◽  
Equilibrium Geometries

In this article we develop a simple statistical model for the prediction of halogen bond interaction energies at equilibrium geometries. The model is based on explicitly correlated coupled cluster results and produces root-mean-squared deviations of 0.14 and 0.28 kcal mol<sup>–1</sup> over separate fitting and validation sets, respectively. We also show how the model can be used to highlight cases where induction or dispersion significantly affect the underlying nature of the interaction.<br>

scholarly journals Quantitative Determination of Vitamins A and E in Ointments Using Raman Spectroscopy

Processes ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 8
Author(s):  
Sylwester Mazurek ◽  
Kamil Pichlak ◽  
Roman Szostak
Keyword(s):  
Vitamin A ◽  
Predictive Ability ◽  
Principal Component ◽  
Data Sets ◽  
Accurate Analysis ◽  
Validation Data ◽  
Pharmaceutical Ingredients ◽  
Relative Standard ◽  
Separate Calibration

A quantitative analysis of vitamins A and E in commercial ointments containing 0.044% and 0.8% (w/w) of active pharmaceutical ingredients, respectively, was performed using partial least squares models based on FT Raman spectra. Separate calibration systems were prepared to determine the amount of vitamin A in a petrolatum base ointment and to quantify vitamins A and E in a eucerin base one. Compositions of the laboratory-prepared and commercial samples were controlled through a principal component analysis. Relative standard errors of prediction were calculated to compare the predictive ability of the obtained regression models. For vitamin A determination, these errors were found to be in the 3.8–5.0% and 5.7–5.9% ranges for the calibration and validation data sets, respectively. In the case of vitamin E modeling, these errors amounted to 3.7% and 4.4%. On the basis of elaborated models, vitamins A and E were successfully quantified in two commercial products with recoveries in the 99–104% range. The obtained data indicate that the Raman technique allows for accurate analysis of the composition of semisolid formulations in their native state, including low dose preparations.

scholarly journals Interactions between large molecules pose a puzzle for reference quantum mechanical methods

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yasmine S. Al-Hamdani ◽  
Péter R. Nagy ◽  
Andrea Zen ◽  
Dennis Barton ◽  
Mihály Kállay ◽  
...  
Keyword(s):  
Experimental Information ◽  
Quantum Mechanical ◽  
Interaction Energies ◽  
Small Organic Molecules ◽  
Large Molecules ◽  
Reference Information ◽  
Mechanical Methods ◽  
Non Covalent Interactions ◽  
Semi Empirical ◽  
Covalent Interactions

AbstractQuantum-mechanical methods are used for understanding molecular interactions throughout the natural sciences. Quantum diffusion Monte Carlo (DMC) and coupled cluster with single, double, and perturbative triple excitations [CCSD(T)] are state-of-the-art trusted wavefunction methods that have been shown to yield accurate interaction energies for small organic molecules. These methods provide valuable reference information for widely-used semi-empirical and machine learning potentials, especially where experimental information is scarce. However, agreement for systems beyond small molecules is a crucial remaining milestone for cementing the benchmark accuracy of these methods. We show that CCSD(T) and DMC interaction energies are not consistent for a set of polarizable supramolecules. Whilst there is agreement for some of the complexes, in a few key systems disagreements of up to 8 kcal mol−1 remain. These findings thus indicate that more caution is required when aiming at reproducible non-covalent interactions between extended molecules.

Non-Covalent Interactions Atlas Benchmark Data Sets 3: Repulsive Contacts

2021 ◽  
Vol 17 (3) ◽  
pp. 1548-1561
Author(s):  
Kristian Kříž ◽  
Martin Nováček ◽  
Jan Řezáč
Keyword(s):  
Data Sets ◽  
Benchmark Data ◽  
Non Covalent Interactions ◽  
Covalent Interactions

scholarly journals The S66 Non-Covalent Interactions Benchmark Reconsidered Using Explicitly Correlated Methods Near the Basis Set Limit

2018 ◽  
Vol 71 (4) ◽  
pp. 238 ◽  
Author(s):  
Manoj K. Kesharwani ◽  
Amir Karton ◽  
Nitai Sylvetsky ◽  
Jan M. L. Martin
Keyword(s):  
Computational Cost ◽  
The Other ◽  
Basis Set ◽  
Basis Sets ◽  
Explicitly Correlated ◽  
Non Covalent Interactions ◽  
Explicitly Correlated Methods ◽  
The One ◽  
High Level ◽  
Covalent Interactions

The S66 benchmark for non-covalent interactions has been re-evaluated using explicitly correlated methods with basis sets near the one-particle basis set limit. It is found that post-MP2 ‘high-level corrections’ are treated adequately well using a combination of CCSD(F12*) with (aug-)cc-pVTZ-F12 basis sets on the one hand, and (T) extrapolated from conventional CCSD(T)/heavy-aug-cc-pV{D,T}Z on the other hand. Implications for earlier benchmarks on the larger S66×8 problem set in particular, and for accurate calculations on non-covalent interactions in general, are discussed. At a slight cost in accuracy, (T) can be considerably accelerated by using sano-V{D,T}Z+ basis sets, whereas half-counterpoise CCSD(F12*)(T)/cc-pVDZ-F12 offers the best compromise between accuracy and computational cost.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

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