ChemInform Abstract: An Integrated Computational Protocol for the Accurate Prediction of EPR and PNMR Parameters of Aminoxyl Radicals in Solution

ChemInform ◽  
2011 ◽  
Vol 42 (13) ◽  
pp. no-no
Author(s):  
Vincenzo Barone ◽  
Paola Cimino ◽  
Alfonso Pedone
Keyword(s):  
Accurate Prediction ◽  
Aminoxyl Radicals ◽  
Computational Protocol

Accurate prediction of 195Pt NMR chemical shifts for a series of Pt(ii) and Pt(iv) antitumor agents by a non-relativistic DFT computational protocol

2014 ◽  
Vol 43 (14) ◽  
pp. 5409-5426 ◽  
Author(s):  
Athanassios C. Tsipis ◽  
Ioannis N. Karapetsas
Keyword(s):  
Dft Calculations ◽  
Anticancer Agents ◽  
Antitumor Agents ◽  
Chemical Shifts ◽  
Accurate Prediction ◽  
Nmr Chemical Shifts ◽  
Relativistic Dft ◽  
Square Planar ◽  
Wide Range ◽  
Computational Protocol

Exhaustive benchmark DFT calculations reveal that the non-relativistic GIAO-PBE0/SARC-ZORA(Pt)∪6-31+G(d)(E) computational protocol predicts accurate 195Pt NMR chemical shifts for a wide range of square planar Pt(ii) and octahedral Pt(iv) anticancer agents.

DOX: A new computational protocol for accurate prediction of the protein-ligand binding structures

2015 ◽  
Vol 37 (3) ◽  
pp. 336-344 ◽  
Author(s):  
Li Rao ◽  
Bo Chi ◽  
Yanliang Ren ◽  
Yongjian Li ◽  
Xin Xu ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Accurate Prediction ◽  
Computational Protocol

Accurate Prediction of Fatigue Life under Random Loading

2012 ◽  
Vol 61 (10) ◽  
pp. 853-859 ◽  
Author(s):  
Norio TAKEDA ◽  
Tomohiro NARUSE
Keyword(s):  
Fatigue Life ◽  
Accurate Prediction ◽  
Random Loading

Combining First-Principles and Data Modeling for the Accurate Prediction of the Refractive Index of Organic Polymers

2017 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
First Principles ◽  
Data Modeling ◽  
Accurate Prediction ◽  
Index Of Refraction ◽  
Structure Theory ◽  
Organic Polymers ◽  
Number Density ◽  
Lorenz Equation ◽  
In Silico Studies ◽  
Wide Range

Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3–1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that an guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and highly economical path to determining the RI values for a wide range of organic polymers.

Actual issues of valuation of businesses of the economic entity

2017 ◽  
Vol 31 (2) ◽  
pp. 156-162 ◽  
Author(s):  
O. V. Schneider
Keyword(s):  
Discount Rate ◽  
Accurate Prediction ◽  
Economic Activities ◽  
Business Valuation ◽  
Economic Entity ◽  
The World ◽  
Definition Of

The article summarizes the main approaches in the definition of business valuation the economic entity. In the process of business valuation, taking into account the risks of financial and economic activities necessary to obtain information on what stage the owner implements the business will receive income. The most difficult task is the impossibility of accurate prediction in determining the level of income and the determination of a discount rate capitalization of future incomes due to the instability of the economy, both in the country and around the world.

scholarly journals Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

2021 ◽  
Author(s):  
Kjell Jorner ◽  
Tore Brinck ◽  
Per-Ola Norrby ◽  
David Buttar
Keyword(s):  
Machine Learning ◽  
Activation Energies ◽  
Accurate Prediction ◽  
Nucleophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Mechanistic Modelling

Hybrid reactivity models, combining mechanistic calculations and machine learning with descriptors, are used to predict barriers for nucleophilic aromatic substitution.

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