scholarly journals Liquid water simulation using hydrogen bond corrected SCAN and neural network potentials.

2021 ◽  
Author(s):  
Timothy Duignan
Keyword(s):  
Neural Network ◽  
Liquid Water ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Satisfactory Description ◽  
Bonding Interaction ◽  
Long Time ◽  
Excellent Description ◽  
Water Simulation ◽  
Structure Of Liquid

Accurately reproducing the structure of liquid water with ab initio molecular dynamics (AIMD) simulation is a crucial first step on the path towards accurately predicting the properties of liquid solutions without relying on experiment. Density functional theory (DFT) is normally used to approximate the forces in these simulations. However, no DFT functional has been shown to give an entirely satisfactory description of the structure of liquid water. Here, I propose a simple correction to the strongly constrained and appropriately normalised (SCAN) DFT functional, that corrects the strength of the hydrogen bonding interaction with a simple exponential potential fitted to dimer energy calculations. The resulting SCAN-CH functional provides an excellent description of the structure of liquid water. Long time scale NPT simulations are enabled by the use of neural network potentials, which demonstrate that the simulations are well converged and that the density of water is also more accurately reproduced with this method.

scholarly journals Understanding the Dipole Moment of Liquid Water from a Self-Attractive Hartree Decomposition

2020 ◽  
Author(s):  
Tianyu Zhu ◽  
Troy Van Voorhis
Keyword(s):  
Dipole Moment ◽  
Dielectric Properties ◽  
Liquid Water ◽  
Density Functional ◽  
Bulk Water ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Design Strategies ◽  
Total Electron Density ◽  
Total Electron

<p>The dipole moment of a single water molecule in liquid water has been a critical concept for understanding water’s dielectric properties. In this work, we investigate the dipole moment of liquid water through a self-attractive Hartree (SAH) decomposition of total electron density computed by density functional theory, on water clusters sampled from ab initio molecular dynamics simulation of bulk water. By adjusting one parameter that controls the degree of density localization, we reveal two distinct pictures of water dipoles that are consistent with bulk dielectric properties: a localized picture with smaller and less polarizable monomer dipoles, and a delocalized picture with larger and more polarizable monomer dipoles. We further uncover that the collective dipole-dipole correlation is stronger in the localized picture and is key to connecting individual dipoles with bulk dielectric properties. Based on these findings, we suggest considering both individual and collective dipole behaviors when studying the dipole moment of liquid water, and propose new design strategies for developing water models.</p>

scholarly journals Understanding the Dipole Moment of Liquid Water from a Self-Attractive Hartree Decomposition

2020 ◽  
Author(s):  
Tianyu Zhu ◽  
Troy Van Voorhis
Keyword(s):  
Dipole Moment ◽  
Dielectric Properties ◽  
Liquid Water ◽  
Density Functional ◽  
Bulk Water ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Design Strategies ◽  
Total Electron Density ◽  
Total Electron

<p>The dipole moment of a single water molecule in liquid water has been a critical concept for understanding water’s dielectric properties. In this work, we investigate the dipole moment of liquid water through a self-attractive Hartree (SAH) decomposition of total electron density computed by density functional theory, on water clusters sampled from ab initio molecular dynamics simulation of bulk water. By adjusting one parameter that controls the degree of density localization, we reveal two distinct pictures of water dipoles that are consistent with bulk dielectric properties: a localized picture with smaller and less polarizable monomer dipoles, and a delocalized picture with larger and more polarizable monomer dipoles. We further uncover that the collective dipole-dipole correlation is stronger in the localized picture and is key to connecting individual dipoles with bulk dielectric properties. Based on these findings, we suggest considering both individual and collective dipole behaviors when studying the dipole moment of liquid water, and propose new design strategies for developing water models.</p>

Ab Initio Molecular Dynamics Simulation of Zinc metalloproteins with Enhanced Self-Organizing Incremental High Dimensional Neural Network

2020 ◽  
Author(s):  
Mingyuan Xu ◽  
Tong Zhu ◽  
John ZH Zhang
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Density Functional ◽  
Structure Refinement ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Neural Network Approach ◽  
Quantum Chemistry Calculations ◽  
Molecular Potentials

<p>Artificial neural network provides the possibility to develop molecular potentials with both the efficiency of the classical molecular mechanics and the accuracy of the quantum chemical methods. In this work, we developed ab initio based neural network potential (NN/MM-RESP-MBG) to perform molecular dynamics study for metalloproteins. The interaction energy, atomic forces, and atomic charges of metal binding group in NN/MM-RESP-MBG are described by a neural network potential trained with energies and forces generated from density functional calculations. Here, we used our recently proposed E-SOI-HDNN model to achieve the automatic construction of reference dataset of metalloproteins and the active learning of neural network potential functions. The predicted energies and atomic forces from the NN potential show excellent agreement with the quantum chemistry calculations. Using this approach, we can perform long time AIMD simulations and structure refinement MD simulation for metalloproteins. In 1 ns AIMD simulation of four common coordination mode of zinc-containing metalloproteins, the statistical average structure is in good agreement with statistic value of PDB Bank database. The neural network approach used in this study can be applied to construct potentials to metalloproteinase catalysis, ligand binding and other important biochemical processes and its salient features can shed light on the development of more accurate molecular potentials for metal ions in other biomacromolecule system. </p>

scholarly journals Ab initio molecular dynamics studies of formic acid dimer colliding with liquid water

2018 ◽  
Vol 20 (36) ◽  
pp. 23717-23725 ◽  
Author(s):  
Vesa Hänninen ◽  
Garold Murdachaew ◽  
Gilbert M. Nathanson ◽  
R. Benny Gerber ◽  
Lauri Halonen
Keyword(s):  
Molecular Dynamics ◽  
Density Functional Theory ◽  
Formic Acid ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Liquid Water ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Functional Theory ◽  
Dynamics Simulations

Ab initio molecular dynamics simulations of formic acid (FA) dimer colliding with liquid water at 300 K have been performed using density functional theory.

scholarly journals Understanding the Dipole Moment of Liquid Water from a Self-Attractive Hartree Decomposition

2020 ◽  
Author(s):  
Tianyu Zhu ◽  
Troy Van Voorhis
Keyword(s):  
Dipole Moment ◽  
Dielectric Properties ◽  
Liquid Water ◽  
Density Functional ◽  
Bulk Water ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Design Strategies ◽  
Total Electron Density ◽  
Total Electron

<p>The dipole moment of a single water molecule in liquid water has been a critical concept for understanding water’s dielectric properties. In this work, we investigate the dipole moment of liquid water through a self-attractive Hartree (SAH) decomposition of total electron density computed by density functional theory, on water clusters sampled from ab initio molecular dynamics simulation of bulk water. By adjusting one parameter that controls the degree of density localization, we reveal two distinct pictures of water dipoles that are consistent with bulk dielectric properties: a localized picture with smaller and less polarizable monomer dipoles, and a delocalized picture with larger and more polarizable monomer dipoles. We further uncover that the collective dipole-dipole correlation is stronger in the localized picture and is key to connecting individual dipoles with bulk dielectric properties. Based on these findings, we suggest considering both individual and collective dipole behaviors when studying the dipole moment of liquid water, and propose new design strategies for developing water models.</p>

Ab Initio Molecular Dynamics Simulation of Zinc metalloproteins with Enhanced Self-Organizing Incremental High Dimensional Neural Network

2020 ◽  
Author(s):  
Mingyuan Xu ◽  
Tong Zhu ◽  
John ZH Zhang
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Density Functional ◽  
Structure Refinement ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Neural Network Approach ◽  
Quantum Chemistry Calculations ◽  
Molecular Potentials

<p>Artificial neural network provides the possibility to develop molecular potentials with both the efficiency of the classical molecular mechanics and the accuracy of the quantum chemical methods. In this work, we developed ab initio based neural network potential (NN/MM-RESP-MBG) to perform molecular dynamics study for metalloproteins. The interaction energy, atomic forces, and atomic charges of metal binding group in NN/MM-RESP-MBG are described by a neural network potential trained with energies and forces generated from density functional calculations. Here, we used our recently proposed E-SOI-HDNN model to achieve the automatic construction of reference dataset of metalloproteins and the active learning of neural network potential functions. The predicted energies and atomic forces from the NN potential show excellent agreement with the quantum chemistry calculations. Using this approach, we can perform long time AIMD simulations and structure refinement MD simulation for metalloproteins. In 1 ns AIMD simulation of four common coordination mode of zinc-containing metalloproteins, the statistical average structure is in good agreement with statistic value of PDB Bank database. The neural network approach used in this study can be applied to construct potentials to metalloproteinase catalysis, ligand binding and other important biochemical processes and its salient features can shed light on the development of more accurate molecular potentials for metal ions in other biomacromolecule system. </p>

scholarly journals Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations

2020 ◽  
Author(s):  
Punyaslok Pattnaik ◽  
Shampa Raghunathan ◽  
Tarun Kalluri ◽  
Prabhakar Bhimalapuram ◽  
C. V. Jawahar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Time Scales ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Experimental Conditions ◽  
The Neural Network ◽  
Dynamics Simulations

<p>The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a DNetFF machine learning model where, the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.</p>

Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations

2020 ◽  
Author(s):  
Punyaslok Pattnaik ◽  
Shampa Raghunathan ◽  
Tarun Kalluri ◽  
Prabhakar Bhimalapuram ◽  
C. V. Jawahar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Time Scales ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Experimental Conditions ◽  
The Neural Network ◽  
Dynamics Simulations

<p>The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a DNetFF machine learning model where, the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.</p>

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