Stable Crystalline Forms of Na Polysulfides: Experiment versus Ab Initio Computational Prediction

2016 ◽  
Vol 22 (10) ◽  
pp. 3355-3360 ◽  
Author(s):  
Gregor Mali ◽  
Manu U. M. Patel ◽  
Matjaž Mazaj ◽  
Robert Dominko
Keyword(s):  
Ab Initio ◽  
Computational Prediction ◽  
Experiment Versus ◽  
Crystalline Forms

scholarly journals PREDICTION OF LOG P AND SPECTRUM OF QUERCETINE, GLUCOSAMINE, AND ANDROGRAPHOLIDE AND ITS CORRELATION WITH LABORATORY ANALYSIS

2016 ◽  
Vol 8 (11) ◽  
pp. 33 ◽  
Author(s):  
Sandra Megantara ◽  
Mutakin Mutakin ◽  
Jutti Levita
Keyword(s):  
Ab Initio ◽  
1H Nmr ◽  
13C Nmr ◽  
Forecast Error ◽  
Computational Prediction ◽  
Percentage Error ◽  
Log P ◽  
P Value ◽  
Absolute Deviation ◽  
R Value

Objective: This study was aimed to confirm the result of computational prediction of log P and spectrum (ultraviolet-visible, 1H-NMR, 13C-NMR) of quercetin, glucosamine and andrographolide with laboratory analysis.Methods: Quercetine, glucosamine and andrographolide, were downloaded from ChemSpider and were geometry optimised. Log P and spectrum were calculated and predicted and the data obtained were compared with laboratory results. The correlation was calculated by employing mean absolute deviation (MAD), mean square error (MSE), mean forecast error (MFE), and mean absolute percentage error (MAPE) parameters.Results: The smallest energy value of geometry optimisation was provided by ab initio method. Log P prediction showed good accuracy, with r-value 0.995 and p-value 0.05 respectively. The error parameters were: MAD 0.19; MSE 0.06; MFE 0.16, and MAPE 8.62%, respectively. Prediction of λ maximum by ab initio, semiempirical, and molecular mechanics were respectively: MAD 2.67, 6.67, and 28.67; MSE 8.67, 45.33, and 830; MFE 2.67, 6.67, and 28.67; and MAPE 1.10%, 2.79%, and 11.99%; r-value 0.997, 0.997, and 0.979; and p-value 0.044, 0.043, and 0.129. 1H-NMR and 13C-NMR spectra prediction were: MAD 0.73 and 1.58; MSE 1.15 and 7.41; MFE 0.27 and 0.69; MAPE 18.35% and 2.68%; r-value 0.942 and 0.986; and p-value 0.001 and 0.001.Conclusion: There is a positive correlation between computational ab initio calculation method with experimental results in predicting log P and spectrum of quercetine, glucosamine, and andrographolide.

scholarly journals Machine learning based energy-free structure predictions of molecules, transition states, and solids

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dominik Lemm ◽  
Guido Falk von Rudorff ◽  
O. Anatole von Lilienfeld
Keyword(s):  
Machine Learning ◽  
Ab Initio ◽  
Chemical Space ◽  
Transition States ◽  
Computational Prediction ◽  
Training Data ◽  
Data Sets ◽  
Prediction Errors ◽  
Optimization Task ◽  
Out Of Sample

AbstractThe computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology. Conventionally, force-fields or ab initio methods determine structure through energy minimization, which is either approximate or computationally demanding. This accuracy/cost trade-off prohibits the generation of synthetic big data sets accounting for chemical space with atomistic detail. Exploiting implicit correlations among relaxed structures in training data sets, our machine learning model Graph-To-Structure (G2S) generalizes across compound space in order to infer interatomic distances for out-of-sample compounds, effectively enabling the direct reconstruction of coordinates, and thereby bypassing the conventional energy optimization task. The numerical evidence collected includes 3D coordinate predictions for organic molecules, transition states, and crystalline solids. G2S improves systematically with training set size, reaching mean absolute interatomic distance prediction errors of less than 0.2 Å for less than eight thousand training structures — on par or better than conventional structure generators. Applicability tests of G2S include successful predictions for systems which typically require manual intervention, improved initial guesses for subsequent conventional ab initio based relaxation, and input generation for subsequent use of structure based quantum machine learning models.

scholarly journals Computational prediction of muon stopping sites using ab initio random structure searching (AIRSS)

2018 ◽  
Vol 148 (13) ◽  
pp. 134114 ◽  
Author(s):  
Leandro Liborio ◽  
Simone Sturniolo ◽  
Dominik Jochym
Keyword(s):  
Ab Initio ◽  
Computational Prediction ◽  
Random Structure

scholarly journals The first example of ab initio calculations of f–f transitions for the case of [Eu(DOTP)]5− complex—experiment versus theory

2016 ◽  
Vol 18 (40) ◽  
pp. 27808-27817 ◽  
Author(s):  
Rafał Janicki ◽  
Andrzej Kędziorski ◽  
Anna Mondry
Keyword(s):  
Ab Initio Calculations ◽  
Ab Initio ◽  
Single Crystals ◽  
Excited States ◽  
Electronic Transitions ◽  
Experiment Versus ◽  
The Eu

Structure, IR and UV-vis-NIR spectra of the [Eu(DOTP)]5− complex in single crystals were studied experimentally. Ab initio calculations of the excited states of the [Eu(DOTP)]5− complex provide new insights into the interpretation of the observed f–f electronic transitions spectra.

Ab initio band theory approach to electron energy loss near edge structures

1988 ◽  
Vol 46 ◽  
pp. 506-507
Author(s):  
Xudong Weng ◽  
O.F. Sankey ◽  
Peter Rez
Keyword(s):  
Ab Initio ◽  
Local Density ◽  
Interpolation Method ◽  
Atomic Orbital ◽  
Plane Waves ◽  
Large Set ◽  
Theory Approach ◽  
Band Theory ◽  
Atomic Orbitals ◽  
Pseudopotential Calculations

Single electron band structure techniques have been applied successfully to the interpretation of the near edge structures of metals and other materials. Among various band theories, the linear combination of atomic orbital (LCAO) method is especially simple and interpretable. The commonly used empirical LCAO method is mainly an interpolation method, where the energies and wave functions of atomic orbitals are adjusted in order to fit experimental or more accurately determined electron states. To achieve better accuracy, the size of calculation has to be expanded, for example, to include excited states and more-distant-neighboring atoms. This tends to sacrifice the simplicity and interpretability of the method.In this paper. we adopt an ab initio scheme which incorporates the conceptual advantage of the LCAO method with the accuracy of ab initio pseudopotential calculations. The so called pscudo-atomic-orbitals (PAO's), computed from a free atom within the local-density approximation and the pseudopotential approximation, are used as the basis of expansion, replacing the usually very large set of plane waves in the conventional pseudopotential method. These PAO's however, do not consist of a rigorously complete set of orthonormal states.

An ab initio study of Se-reacted GaAs(0 0 1) surfaces

1998 ◽  
Vol 184-185 (1-2) ◽  
pp. 80-84 ◽  
Author(s):  
W Faschinger
Keyword(s):  
Ab Initio ◽  
Ab Initio Study
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