Free energy barriers for CO2and N2in zeolite NaKA: an ab initio molecular dynamics approach

2014 ◽  
Vol 16 (1) ◽  
pp. 166-172 ◽  
Author(s):  
Amber Mace ◽  
Kari Laasonen ◽  
Aatto Laaksonen
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Ab Initio ◽  
Ab Initio Molecular Dynamics ◽  
Energy Barriers

Ab initio mechanism revealing for tricalcium silicate dissolution

2021 ◽  
Author(s):  
Yunjian Li ◽  
Hui Pan ◽  
Xing Ming ◽  
Zongjin Li
Keyword(s):  
Free Energy ◽  
Ab Initio ◽  
Calcium Ions ◽  
Tricalcium Silicate ◽  
Proton Exchange ◽  
Reaction Pathways ◽  
Ab Initio Molecular Dynamics ◽  
Energy Barriers ◽  
Silicate Dissolution ◽  
Quantitative Analyses

Abstract Dissolution of mineral in water is ubiquitous in nature and industry, especially for the calcium silicate species. However, the behavior of such a complex chemical reaction is still unclear at atomic level. Here, we show that the ab initio molecular dynamics and metadynamics simulations enable quantitative analyses of reaction pathways, and the thermodynamics and kinetics of calcium ion dissolution from the tricalcium silicate (Ca3SiO5) surface. The calcium sites with different coordination environment leads to different reaction pathways and free energy barriers. The low free energy barriers lead to that the detachment of calcium ions is a ligand exchange and auto-catalytic process. Moreover, the water adsorption, proton exchange and diffusion of water into the surface layer accelerate the leaching of calcium ions from the surface step by step. The discovery in this work thus would be a landmark for revealing the mechanism of cement hydration.

scholarly journals Water Dipole and Quadrupole Moment Contributions to the Ion Hydration Free Energy by the Deep Neural Network Trained with Ab Initio Molecular Dynamics Data

2020 ◽  
Author(s):  
YU SHI ◽  
Carrie C. Doyle ◽  
Thomas L. Beck
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Free Energy ◽  
Ab Initio ◽  
Deep Neural Network ◽  
Ab Initio Molecular Dynamics ◽  
Quadrupole Moments ◽  
Hydration Free Energy ◽  
Ion Hydration ◽  
Water Dipole

<div>We report a calculation scheme on water molecular dipole and quadrupole moments in the liquid phase through a Deep Neural Network (DNN) model. Employing the the Maximally Localized Wannier Functions (MLWF) for the valence electrons, we obtain the water moments through a post-process on trajectories from \textit{ab-initio} molecular dynamics (AIMD) simulations at the density functional theory (DFT) level. In the framework of the deep potential molecular dynamics (DPMD), we develop a scheme to train a DNN with the AIMD moments data. Applying the model, we calculate the contributions from water dipole and quadrupole moments to the electrostatic potential at the center of a cavity of radius 4.1 \AA\ as -3.87 V, referenced to the average potential in the bulk-like liquid region.</div><div>To unravel the ion-independent water effective local potential contribution to the ion hydration free energy, we estimate the 3rd cumulant term as -0.22 V from simulations totally over 6 ns, a time-scale inaccessible for AIMD calculations. </div>

scholarly journals Exploring the free energy surface using ab initio molecular dynamics

2016 ◽  
Vol 144 (16) ◽  
pp. 164101 ◽  
Author(s):  
Amit Samanta ◽  
Miguel A. Morales ◽  
Eric Schwegler
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Ab Initio ◽  
Energy Surface ◽  
Ab Initio Molecular Dynamics ◽  
Free Energy Surface

scholarly journals Interconversion of hydrated protons at the interface between liquid water and platinum

2019 ◽  
Vol 21 (11) ◽  
pp. 5932-5940 ◽  
Author(s):  
Peter S. Rice ◽  
Yu Mao ◽  
Chenxi Guo ◽  
P. Hu
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Ab Initio ◽  
Liquid Water ◽  
Hydrogen Transfer ◽  
Umbrella Sampling ◽  
Energy Barriers ◽  
Hydrated Protons

The free energy barriers for hydrogen transfer at the H2O/Pt(111) interface calculated usingab initiomolecular dynamics and umbrella sampling.

scholarly journals Ultrafast proton transport in water-methanol mixtures

2019 ◽  
Vol 205 ◽  
pp. 09004
Author(s):  
Maria Ekimova ◽  
Felix Hoffmann ◽  
Gul Bekcioglu-Neff ◽  
Aidan Rafferty ◽  
Erik T. J. Nibbering ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Proton Transport ◽  
Ab Initio Molecular Dynamics ◽  
Transport Pathway ◽  
Pump Probe ◽  
Dynamics Simulations

Femtosecond UV/IR pump-probe experiments and ab initio molecular dynamics simulations of 7-hydroxyquinoline in water-methanol mixtures demonstrate an unexpectedly dominant OH-/CH3O- transport pathway but consistent with a solvent-dependent photoacidity free energy-reactivity correlation behaviour.

scholarly journals Water Dipole and Quadrupole Moment Contributions to the Ion Hydration Free Energy by the Deep Neural Network Trained with Ab Initio Molecular Dynamics Data

2020 ◽  
Author(s):  
YU SHI ◽  
Carrie C. Doyle ◽  
Thomas L. Beck
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Free Energy ◽  
Ab Initio ◽  
Deep Neural Network ◽  
Ab Initio Molecular Dynamics ◽  
Quadrupole Moments ◽  
Hydration Free Energy ◽  
Ion Hydration ◽  
Water Dipole

<div>We report a calculation scheme on water molecular dipole and quadrupole moments in the liquid phase through a Deep Neural Network (DNN) model. Employing the the Maximally Localized Wannier Functions (MLWF) for the valence electrons, we obtain the water moments through a post-process on trajectories from \textit{ab-initio} molecular dynamics (AIMD) simulations at the density functional theory (DFT) level. In the framework of the deep potential molecular dynamics (DPMD), we develop a scheme to train a DNN with the AIMD moments data. Applying the model, we calculate the contributions from water dipole and quadrupole moments to the electrostatic potential at the center of a cavity of radius 4.1 \AA\ as -3.87 V, referenced to the average potential in the bulk-like liquid region.</div><div>To unravel the ion-independent water effective local potential contribution to the ion hydration free energy, we estimate the 3rd cumulant term as -0.22 V from simulations totally over 6 ns, a time-scale inaccessible for AIMD calculations. </div>

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