scholarly journals Ab Initio Calculations of Free Energy of Activation at Multiple Electronic Structure Levels Made Affordable: An Effective Combination of Perturbation Theory and Machine Learning

2020 ◽  
Vol 16 (10) ◽  
pp. 6049-6060
Author(s):  
Tomáš Bučko ◽  
Monika Gešvandtnerová ◽  
Dario Rocca
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Perturbation Theory ◽  
Electronic Structure ◽  
Ab Initio Calculations ◽  
Ab Initio ◽  
Energy Of Activation ◽  
Free Energy Of Activation ◽  
Effective Combination

scholarly journals Ab initio calculations of free energy of activation at multiple electronic structure levels made affordable: An effective combination of perturbation theory and machine learning

2020 ◽  
Author(s):  
Tomas Bucko ◽  
Monika Gešvandtnerová ◽  
Dario Rocca
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Perturbation Theory ◽  
Ab Initio ◽  
Single Point ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Free Energy Of Activation ◽  
Ab Initio Theory

<div>While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the training of the machine learning model requires only a small amount of data and does not need to be performed again when the temperature is decreased. The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal approximation of density functional theory, free energies based on significantly more expensive non-local van der Waals and hybrid functionals are obtained with only a few tens of additional single point calculations. In this way this work paves the route to quick free energy calculations using different levels of theory or approximations that would be too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations.</div>

scholarly journals Ab initio calculations of free energy of activation at multiple electronic structure levels made affordable: An effective combination of perturbation theory and machine learning

2020 ◽  
Author(s):  
Tomas Bucko ◽  
Monika Gešvandtnerová ◽  
Dario Rocca
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Perturbation Theory ◽  
Ab Initio ◽  
Single Point ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Free Energy Of Activation ◽  
Ab Initio Theory

<div>While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the training of the machine learning model requires only a small amount of data and does not need to be performed again when the temperature is decreased. The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal approximation of density functional theory, free energies based on significantly more expensive non-local van der Waals and hybrid functionals are obtained with only a few tens of additional single point calculations. In this way this work paves the route to quick free energy calculations using different levels of theory or approximations that would be too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations.</div>

Ab Initio Free Energy Calculations at Multiple Electronic Structure Levels Made Affordable: An Effective Combination of Perturbation Theory and Machine Learning

2020 ◽  
Author(s):  
Tomas Bucko ◽  
Monika Gešvandtnerová ◽  
Dario Rocca
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Perturbation Theory ◽  
Ab Initio ◽  
Single Point ◽  
Free Energy Calculations ◽  
Thermodynamic Quantities ◽  
Free Energies ◽  
Energy Calculations ◽  
Ab Initio Theory

<div>While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the </div><div>training of the machine learning model requires only a small amount of data and does not need to be </div><div>performed again when the temperature is decreased.</div><div>The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the </div><div>proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal</div><div>approximation of density functional theory, free energies based on significantly </div><div>more expensive non-local van der Waals and hybrid functionals are obtained with only a few tens</div><div>of additional single point calculations. In this way this work paves the route to</div><div>quick free energy calculations using different levels of theory or approximations that would be</div><div>too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations.</div>

scholarly journals Free energy of ( CoxMn1−x)3O4 mixed phases from machine-learning-enhanced ab initio calculations

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Suzanne K. Wallace ◽  
Anton S. Bochkarev ◽  
Ambroise van Roekeghem ◽  
Javier Carrasco ◽  
Alexander Shapeev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Ab Initio Calculations ◽  
Ab Initio

Viscosity of Dimethyl Sulfoxide + 1,4 Dimethylbenzene System

2008 ◽  
Vol 59 (1) ◽  
pp. 45-48
Author(s):  
Oana Ciocirlan ◽  
Olga Iulian
Keyword(s):  
Free Energy ◽  
Dimethyl Sulfoxide ◽  
Atmospheric Pressure ◽  
Entire Range ◽  
Polynomial Equation ◽  
Energy Of Activation ◽  
Excess Gibbs Free Energy ◽  
Experimental Measurements ◽  
Gibbs Energy Of Activation ◽  
Free Energy Of Activation

This paper reports the viscosities measurements for the binary system dimethyl sulfoxide + 1,4-dimethylbenzene over the entire range of mole fraction at 298.15, 303.15, 313.15 and 323.15 K and atmospheric pressure. The experimental viscosities were correlated with the equations of Grunberg-Nissan, Katti-Chaudhri, Hind, Soliman and McAllister; the adjustable binary parameters have been obtained. The excess Gibbs energy of activation of viscous flow (G*E) has been calculated from the experimental measurements and the results were fitted to Redlich-Kister polynomial equation. The obtained negative excess Gibbs free energy of activation and negative Grunberg-Nissan interaction parameter are discussed in structural and interactional terms.

Flexible H2O2in Water: Electronic Structure from Photoelectron Spectroscopy and Ab Initio Calculations

2011 ◽  
Vol 115 (23) ◽  
pp. 6239-6249 ◽  
Author(s):  
Stephan Thürmer ◽  
Robert Seidel ◽  
Bernd Winter ◽  
Milan Ončák ◽  
Petr Slavíček
Keyword(s):  
Electronic Structure ◽  
Ab Initio Calculations ◽  
Ab Initio ◽  
Photoelectron Spectroscopy
Sign in / Sign up

Export Citation Format

Share Document