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 Mapping the Structure of Oxygen-Doped Wurtzite Aluminum Nitride Coatings from Ab Initio Random Structure Search and Experiments

2021 ◽  
Vol 13 (4) ◽  
pp. 5762-5771
Author(s):  
Piero Gasparotto ◽  
Maria Fischer ◽  
Daniele Scopece ◽  
Maciej O. Liedke ◽  
Maik Butterling ◽  
...  
Keyword(s):  
Aluminum Nitride ◽  
Ab Initio ◽  
Random Structure ◽  
Structure Search ◽  
Nitride Coatings

Nature of electronic topological transition and superconductivity in bismuth under high pressure from ab initio random structure searching

2021 ◽  
Vol 200 ◽  
pp. 110806
Author(s):  
Wanaruk Chaimayo ◽  
Prutthipong Tsuppayakorn-aek ◽  
Prayoonsak Pluengphon ◽  
Komsilp Kotmool ◽  
Teerachote Pakornchote ◽  
...  
Keyword(s):  
High Pressure ◽  
Ab Initio ◽  
Random Structure ◽  
Topological Transition ◽  
Electronic Topological Transition

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.

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 Ab initio random structure searching of organic molecular solids: assessment and validation against experimental data

2017 ◽  
Vol 19 (38) ◽  
pp. 25949-25960 ◽  
Author(s):  
Miri Zilka ◽  
Dmytro V. Dudenko ◽  
Colan E. Hughes ◽  
P. Andrew Williams ◽  
Simone Sturniolo ◽  
...  
Keyword(s):  
Experimental Data ◽  
Ab Initio ◽  
Crystal Structures ◽  
Mas Nmr ◽  
Molecular Solids ◽  
Random Structure ◽  
Aminobenzoic Acid ◽  
X Ray Diffraction ◽  
X Ray ◽  
Acid Comparison

The AIRSS method generates crystal structures for m-aminobenzoic acid; comparison is made to experimental powder X-ray diffraction and MAS NMR.

scholarly journals Hunting for hydrogen: random structure searching and prediction of NMR parameters of hydrous wadsleyite

2016 ◽  
Vol 18 (15) ◽  
pp. 10173-10181 ◽  
Author(s):  
Robert F. Moran ◽  
David McKay ◽  
Chris J. Pickard ◽  
Andrew J. Berry ◽  
John M. Griffin ◽  
...  
Keyword(s):  
Ab Initio ◽  
Experimental Comparison ◽  
Random Structure ◽  
Nmr Parameters

Ab initio random structure searching is employed to generate candidate structures of hydrous wadsleyite, predicting NMR parameters for experimental comparison.

COMPUTATIONAL PREDICTION OF RADIATIVE PROPERTIES OF POLYMER CLOSED-CELL FOAMS WITH RANDOM STRUCTURE

2013 ◽  
Vol 16 (2) ◽  
pp. 137-154 ◽  
Author(s):  
Remi Coquard ◽  
Jaona Randrianalisoa ◽  
Dominique Doermann Baillis
Keyword(s):  
Computational Prediction ◽  
Radiative Properties ◽  
Random Structure ◽  
Closed Cell

scholarly journals Ab initio Structure Prediction Methods for Battery Materials : A review of recent computational efforts to predict the atomic level structure and bonding in materials for rechargeable batteries

2020 ◽  
Vol 64 (2) ◽  
pp. 103-118 ◽  
Author(s):  
Angela F. Harper ◽  
Matthew L. Evans ◽  
James P. Darby ◽  
Bora Karasulu ◽  
Can P. Koçer ◽  
...  
Keyword(s):  
Ab Initio ◽  
Structure Prediction ◽  
Density Functional ◽  
Theoretical Calculations ◽  
Level Structure ◽  
Lithium Ion ◽  
Rechargeable Batteries ◽  
Random Structure ◽  
Voltage Profile ◽  
Battery Materials

Portable electronic devices, electric vehicles and stationary energy storage applications, which encourage carbon-neutral energy alternatives, are driving demand for batteries that have concurrently higher energy densities, faster charging rates, safer operation and lower prices. These demands can no longer be met by incrementally improving existing technologies but require the discovery of new materials with exceptional properties. Experimental materials discovery is both expensive and time consuming: before the efficacy of a new battery material can be assessed, its synthesis and stability must be well-understood. Computational materials modelling can expedite this process by predicting novel materials, both in stand-alone theoretical calculations and in tandem with experiments. In this review, we describe a materials discovery framework based on density functional theory (DFT) to predict the properties of electrode and solid-electrolyte materials and validate these predictions experimentally. First, we discuss crystal structure prediction using the Ab initio random structure searching (AIRSS) method. Next, we describe how DFT results allow us to predict which phases form during electrode cycling, as well as the electrode voltage profile and maximum theoretical capacity. We go on to explain how DFT can be used to simulate experimentally measurable properties such as nuclear magnetic resonance (NMR) spectra and ionic conductivities. We illustrate the described workflow with multiple experimentally validated examples: materials for lithium-ion and sodium-ion anodes and lithium-ion solid electrolytes. These examples highlight the power of combining computation with experiment to advance battery materials research.

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