scholarly journals Absolute ion hydration free energy scale and the surface potential of water via quantum simulation

2020 ◽  
Vol 117 (48) ◽  
pp. 30151-30158
Author(s):  
Yu Shi ◽  
Thomas L. Beck
Keyword(s):  
Free Energy ◽  
Long Range ◽  
Electric Fields ◽  
Surface Potential ◽  
Energy Scale ◽  
Potential Contribution ◽  
Hydration Free Energy ◽  
Ion Hydration ◽  
Ion Distributions ◽  
Interfacial Potential

With a goal of determining an absolute free energy scale for ion hydration, quasi-chemical theory and ab initio quantum mechanical simulations are employed to obtain an accurate value for the bulk hydration free energy of the Na+ion. The free energy is partitioned into three parts: 1) the inner-shell or chemical contribution that includes direct interactions of the ion with nearby waters, 2) the packing free energy that is the work to produce a cavity of size λ in water, and 3) the long-range contribution that involves all interactions outside the inner shell. The interfacial potential contribution to the free energy resides in the long-range term. By averaging cation and anion data for that contribution, cumulant terms of all odd orders in the electrostatic potential are removed. The computed total is then the bulk hydration free energy. Comparison with the experimentally derived real hydration free energy produces an effective surface potential of water in the range −0.4 to −0.5 V. The result is consistent with a variety of experiments concerning acid–base chemistry, ion distributions near hydrophobic interfaces, and electric fields near the surface of water droplets.

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>

Hydrogen bonding solvent effect on the basicity of primary amines CH3NH2, C2H5NH2, and CF3CH2NH2

1981 ◽  
Vol 59 (1) ◽  
pp. 151-155 ◽  
Author(s):  
Yan K. Lau ◽  
P. Kebarle
Keyword(s):  
Free Energy ◽  
Energy Difference ◽  
Ion Source ◽  
Primary Amines ◽  
Hydration Free Energy ◽  
Ion Hydration ◽  
Free Energy Difference ◽  
Source Pressure ◽  
Pulsed Electron Beam ◽  
Good Agreement

The equilibria RNH3+(H2O)n−1 + H2O = RNH3+(H2O)n were measured for R = CH3, C2H5, and CF3CH2 from n = 1 to n = 3 with a pulsed electron beam high ion source pressure mass spectrometer. The proton and hydrate transfer equilibria CH3NH3+(H2O)n + C2H5NH2 = CH3NH2 + C2H5NH3+(H2O)n were measured for n = 0 to n = 3. These data allow the evaluation of ΔH0 and ΔG0 for the reactions: R0NH3+(H2O)n + RNH3+ = R0NH3+ + RNH3+(H2O)n. ΔH0 = δΔH00,n(RNH3+), ΔG = δΔG00,n(RNH3+). These data are compared with δΔE0,3 (STO-3G) evaluated by Hehre and Taft. In general good agreement is observed at n = 3. The δΔH00,3(RNH3+) ≈ δΔE0,3(RNH3+) are also found close to the ion hydration free energy difference in aqueous solutions.

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 A Review of Basic Energy Reconstruction Techniques in Liquid Xenon and Argon Detectors for Dark Matter and Neutrino Physics Using NEST

Instruments ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Matthew Szydagis ◽  
Grant A. Block ◽  
Collin Farquhar ◽  
Alexander J. Flesher ◽  
Ekaterina S. Kozlova ◽  
...  
Keyword(s):  
Dark Matter ◽  
Neutrino Physics ◽  
Electric Fields ◽  
Direct Detection ◽  
Profile Likelihood ◽  
Liquid Argon ◽  
Energy Scale ◽  
Liquid Xenon ◽  
Nuclear Recoils ◽  
Available Information

Detectors based upon the noble elements, especially liquid xenon as well as liquid argon, as both single- and dual-phase types, require reconstruction of the energies of interacting particles, both in the field of direct detection of dark matter (weakly interacting massive particles WIMPs, axions, etc.) and in neutrino physics. Experimentalists, as well as theorists who reanalyze/reinterpret experimental data, have used a few different techniques over the past few decades. In this paper, we review techniques based on solely the primary scintillation channel, the ionization or secondary channel available at non-zero drift electric fields, and combined techniques that include a simple linear combination and weighted averages, with a brief discussion of the application of profile likelihood, maximum likelihood, and machine learning. Comparing results for electron recoils (beta and gamma interactions) and nuclear recoils (primarily from neutrons) from the Noble Element Simulation Technique (NEST) simulation to available data, we confirm that combining all available information generates higher-precision means, lower widths (energy resolution), and more symmetric shapes (approximately Gaussian) especially at keV-scale energies, with the symmetry even greater when thresholding is addressed. Near thresholds, bias from upward fluctuations matters. For MeV-GeV scales, if only one channel is utilized, an ionization-only-based energy scale outperforms scintillation; channel combination remains beneficial. We discuss here what major collaborations use.

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