scholarly journals Accurate and precise ab initio anharmonic free-energy calculations for metallic crystals: Application to hcp Fe at high temperature and pressure

2017 ◽  
Vol 96 (1) ◽  
Author(s):  
Sabry G. Moustafa ◽  
Andrew J. Schultz ◽  
Eva Zurek ◽  
David A. Kofke
Keyword(s):  
Free Energy ◽  
High Temperature ◽  
Ab Initio ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
High Temperature And Pressure ◽  
Temperature And Pressure

Exploring the Free Energy and Conformational Landscape of tRNA at High Temperature and Pressure

ChemPhysChem ◽  
2014 ◽  
Vol 16 (1) ◽  
pp. 138-146 ◽  
Author(s):  
Caroline Schuabb ◽  
Melanie Berghaus ◽  
Christopher Rosin ◽  
Roland Winter
Keyword(s):  
Free Energy ◽  
High Temperature ◽  
High Temperature And Pressure ◽  
Conformational Landscape ◽  
Temperature And Pressure

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>

Ab initio free energy calculations on the polymorphs of iron at core conditions

2000 ◽  
Vol 117 (1-4) ◽  
pp. 123-137 ◽  
Author(s):  
Lidunka Vočadlo ◽  
John Brodholt ◽  
Dario Alfè ◽  
Michael J. Gillan ◽  
Geoffrey D. Price
Keyword(s):  
Free Energy ◽  
Ab Initio ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Core Conditions

scholarly journals Full atomistic reaction mechanism with kinetics for CO reduction on Cu(100) from ab initio molecular dynamics free-energy calculations at 298 K

2017 ◽  
Vol 114 (8) ◽  
pp. 1795-1800 ◽  
Author(s):  
Tao Cheng ◽  
Hai Xiao ◽  
William A. Goddard
Keyword(s):  
Free Energy ◽  
Reaction Mechanism ◽  
Ab Initio ◽  
Rational Design ◽  
Major Product ◽  
Free Energy Calculations ◽  
Ab Initio Molecular Dynamics ◽  
Applied Potential ◽  
Hydrogen Electrode ◽  
Energy Calculations

A critical step toward the rational design of new catalysts that achieve selective and efficient reduction of CO2to specific hydrocarbons and oxygenates is to determine the detailed reaction mechanism including kinetics and product selectivity as a function of pH and applied potential for known systems. To accomplish this, we apply ab initio molecular metadynamics simulations (AIMμD) for the water/Cu(100) system with five layers of the explicit solvent under a potential of −0.59 V [reversible hydrogen electrode (RHE)] at pH 7 and compare with experiment. From these free-energy calculations, we determined the kinetics and pathways for major products (ethylene and methane) and minor products (ethanol, glyoxal, glycolaldehyde, ethylene glycol, acetaldehyde, ethane, and methanol). For an applied potential (U) greater than −0.6 V (RHE) ethylene, the major product, is produced via the Eley–Rideal (ER) mechanism using H2O +e–. The rate-determining step (RDS) is C–C coupling of two CO, with ΔG‡= 0.69 eV. For an applied potential less than −0.60 V (RHE), the rate of ethylene formation decreases, mainly due to the loss of CO surface sites, which are replaced by H*. The reappearance of C2H4along with CH4atUless than −0.85 V arises from *CHO formation produced via an ER process of H* with nonadsorbed CO (a unique result). This *CHO is the common intermediate for the formation of both CH4and C2H4. These results suggest that, to obtain hydrocarbon products selectively and efficiency at pH 7, we need to increase the CO concentration by changing the solvent or alloying the surface.

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>

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