Comment on “Ab initio molecular dynamics calculation of ion hydration free energies” [J. Chem. Phys. 130, 204507 (2009)]

2010 ◽  
Vol 133 (4) ◽  
pp. 047103 ◽  
Author(s):  
Edward S. Chen ◽  
Edward C. M. Chen
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Chem Phys ◽  
Ab Initio Molecular Dynamics ◽  
Free Energies ◽  
Ion Hydration ◽  
Dynamics Calculation

scholarly journals Ab initio molecular dynamics calculations of ion hydration free energies

2009 ◽  
Vol 130 (20) ◽  
pp. 204507 ◽  
Author(s):  
Kevin Leung ◽  
Susan B. Rempe ◽  
O. Anatole von Lilienfeld
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Ab Initio Molecular Dynamics ◽  
Free Energies ◽  
Ion Hydration ◽  
Molecular Dynamics Calculations

scholarly journals Correction: Penetrating probability and cross section of the Li+–C60 encapsulation process through an ab initio molecular dynamics investigation

2018 ◽  
Vol 20 (41) ◽  
pp. 26786-26786
Author(s):  
Thi H. Ho ◽  
Yoshiyuki Kawazoe ◽  
Hung M. Le
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Cross Section ◽  
Chem Phys ◽  
Ab Initio Molecular Dynamics ◽  
Encapsulation Process ◽  
Phys Chem Chem Phys

Correction for ‘Penetrating probability and cross section of the Li+–C60 encapsulation process through an ab initio molecular dynamics investigation’ by Thi H. Ho et al., Phys. Chem. Chem. Phys., 2018, 20, 7007–7013.

Erratum: “Ab initio molecular dynamics simulation of liquid Al88Si12 alloys” [J. Chem. Phys. 122, 034508 (2005)]

2008 ◽  
Vol 129 (8) ◽  
pp. 089901
Author(s):  
Songyou Wang ◽  
C. Z. Wang ◽  
Feng-Chuan Chuang ◽  
James R. Morris ◽  
K. M. Ho
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Ab Initio ◽  
Chem Phys ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics

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 Interplay of physically different properties leading to challenges in separating lanthanide cations – an ab initio molecular dynamics and experimental study

2021 ◽  
Vol 23 (10) ◽  
pp. 5750-5759
Author(s):  
Kevin Leung ◽  
Anastasia G. Ilgen ◽  
Louise J. Criscenti
Keyword(s):  
Molecular Dynamics ◽  
Experimental Study ◽  
Ab Initio ◽  
Ab Initio Molecular Dynamics ◽  
Free Energies ◽  
Separation Technology ◽  
Silica Surfaces ◽  
Lanthanide Cations ◽  
Trivalent Cations

Lanthanide trivalent cations have similar adsorption free energies on silica surfaces, making it difficult to separate them. We elucidate the fundamental scientific reasons for this behavior. Our findings inform future separation technology.

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