scholarly journals Development and Application of a Single Neural Network Potential for IRMOF-n (n=1,4,6,7,10)

2021 ◽  
Author(s):  
Omer Tayfuroglu ◽  
Abdul Kadir Kocak ◽  
Yunus Zorlu
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Thermal Expansion ◽  
Density Of States ◽  
Mechanical Strain ◽  
Computational Cost ◽  
Negative Thermal Expansion ◽  
Md Simulations ◽  
Ab Initio Molecular Dynamics ◽  
Lattice Constants

Metal‑organic frameworks (MOFs) with their exceptional porous and organized structures have been subject of numerous applications. Predicting macroscopic properties from atomistic simulations require the most accurate force fields, which is still a major problem due to MOFs’ hybrid structures governed by covalent, ionic and dispersion forces. Application of ab‑initio molecular dynamics to such large periodic systems are thus beyond the current computational power. Therefore, alternative strategies must be developed to reduce computational cost without losing reliability. In this work, we describe the construction of a neural network potential (NNP) for IRMOF‑n series (n=1,4,7,10) trained by PBE-D4/def2-TZVP reference data of MOF fragments. We validated the resulting NNP on both fragments and bulk MOF structures by prediction of properties such as equilibrium lattice constants, phonon density of states and linker orientation. The energy and force RMSE values for the fragments are only 0.0017 eV/atom and 0.15 eV/Å, respectively. The NNP predicted equilibrium lattice constants of bulk structures, which are not included in training, are off by only 0.2-2.4% from experimental results. Moreover, our fragment trained NNP greatly predicts phenylene ring torsional energy barrier, equilibrium bond distances and vibrational density of states of bulk MOFs. Furthermore, NNP allows us to investigate unusual behaviors of selected MOFs such as the thermal expansion properties and the effect of mechanical strain on the adsorption of hydrogen and methane molecules. The NNP based molecular dynamics (MD) simulations suggest the IRMOF‑4 and IRMOF‑7 to have positive‑to‑negative thermal expansion coefficients while the rest to have only negative thermal expansion under the studied temperatures of 200 K to 400 K. The deformation of bulk structure by reduction of unit cell volume has shown to increase volumetric methane uptake in IRMOF‑1 but decrease in IRMOF‑7 due to the steric hindrance.

scholarly journals Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Cheol Woo Park ◽  
Mordechai Kornbluth ◽  
Jonathan Vandermause ◽  
Chris Wolverton ◽  
Boris Kozinsky ◽  
...  
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Prediction Accuracy ◽  
High Performance ◽  
Md Simulation ◽  
Computational Cost ◽  
Ab Initio Molecular Dynamics ◽  
Force Prediction ◽  
Computational Speed ◽  
Material Systems

AbstractRecently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

scholarly journals A hierarchical Bayesian framework for force field selection in molecular dynamics simulations

2016 ◽  
Vol 374 (2060) ◽  
pp. 20150032 ◽  
Author(s):  
S. Wu ◽  
P. Angelikopoulos ◽  
C. Papadimitriou ◽  
R. Moser ◽  
P. Koumoutsakos
Keyword(s):  
Experimental Data ◽  
Molecular Dynamics ◽  
Force Field ◽  
Computational Cost ◽  
Md Simulations ◽  
Bayesian Framework ◽  
Underlying Structure ◽  
Hierarchical Bayesian ◽  
Wide Range ◽  
Selection Of

We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different environmental conditions with the corresponding variability in experimental data. The high computational cost associated with the hierarchical Bayesian framework is reduced by orders of magnitude through a parallelized Transitional Markov Chain Monte Carlo method combined with the Laplace Asymptotic Approximation. The suitability of the hierarchical approach is demonstrated by performing MD simulations with prescribed parameters to obtain data for transport coefficients under different conditions, which are then used to infer and evaluate the parameters of the MD model. We demonstrate the selection of MD models based on experimental data and verify that the hierarchical model can accurately quantify the uncertainty across experiments; improve the posterior probability density function estimation of the parameters, thus, improve predictions on future experiments; identify the most plausible force field to describe the underlying structure of a given dataset. The framework and associated software are applicable to a wide range of nanoscale simulations associated with experimental data with a hierarchical structure.

Geometry of OH⋯O interactions in the liquid state of linear alcohols from ab initio molecular dynamics simulations

2020 ◽  
Vol 22 (12) ◽  
pp. 6690-6697 ◽  
Author(s):  
Aman Jindal ◽  
Sukumaran Vasudevan
Keyword(s):  
Molecular Dynamics ◽  
Hydrogen Bonding ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Liquid State ◽  
Crystalline State ◽  
Md Simulations ◽  
Ab Initio Molecular Dynamics ◽  
Linear Alcohols ◽  
Dynamics Simulations

Hydrogen bonding OH···O geometries in the liquid state of linear alcohols, derived from ab initio MD simulations, show no change from methanol to pentanol, in contrast to that observed in their crystalline state.

Mechanical Properties of α-Helices Estimated Using Molecular Dynamics and Finite Element Simulations

2008 ◽  
Author(s):  
Peyman Honarmandi ◽  
Philip Bransford ◽  
Roger D. Kamm
Keyword(s):  
Mechanical Properties ◽  
Molecular Dynamics ◽  
Finite Element ◽  
Solid Mechanics ◽  
Normal Mode Analysis ◽  
Computational Cost ◽  
Md Simulations ◽  
Elastic Stiffness ◽  
Mode Analysis ◽  
Molecular Volume

Mechanical properties of biomolecules and their response to mechanical forces may be studied using Molecular Dynamics (MD) simulations. However, high computational cost is a primary drawback of MD simulations. This paper presents a computational framework based on the integration of the Finite Element Method (FEM) with MD simulations to calculate the mechanical properties of polyalanine α-helix proteins. In this method, proteins are treated as continuum elastic solids with molecular volume defined exclusively by their atomic surface. Therefore, all solid mechanics theories would be applicable for the presumed elastic media. All-atom normal mode analysis is used to calculate protein’s elastic stiffness as input to the FEM. In addition, constant force molecular dynamics (CFMD) simulations can be used to predict other effective mechanical properties, such as the Poisson’s Ratio. Force versus strain data help elucidate the mechanical behavior of α-helices upon application of constant load. The proposed method may be useful in identifying the mechanical properties of any protein or protein assembly with known atomic structure.

Molecular Dynamics and Multiscale Simulations of Nanoindentation and Nanoscratch

2010 ◽  
Author(s):  
Peng-zhe Zhu ◽  
Hui Wang ◽  
Yuan-zhong Hu
Keyword(s):  
Molecular Dynamics ◽  
Computational Cost ◽  
Three Dimensional ◽  
Multiscale Model ◽  
Md Simulations ◽  
System Size ◽  
Multiscale Simulations ◽  
First Case ◽  
Chip Volume ◽  
Indenter Shape

Three-dimensional molecular dynamics (MD) simulations have been performed to investigate behaviors of nanoindentation and nano-scratch. The first case concerns the effects of material defect on the nanoindentation of nickel thin film. The defect is modeled by a spherical void embedded in the substrate and located under the surface of indentation. The simulation results reveal that compared to the case without defect, the presence of the void softens the material and allows for larger indentation depth at a given load. MD simulations are then performed for nano-scratch of single crystal copper, with emphasis on the effect of indenter shape (sharp and blunt) on the substrate deformation. The results show that the blunt indenter causes larger deformation region and much more dislocations at both the indentation and scratch stages. It is also found that during the scratching stage the blunt indenter results in larger chip volume in front of the indenter and gives rise to more friction than the sharp indenter. The scope of the simulations has been extended by introducing a multiscale model which couples MD simulations with Finite Element Method (FEM), and multiscale simulations are performed for two-dimensional nanoindentation of copper. The model has been validated by well-consistent load-depth curves obtained from both multiscale and full MD simulations, and by good continuity of deformation observed in the handshake region. The simulations also reveal that indenter radius and indentation velocity significantly affect the nanoindentation behavior. By use of multiscale method, the system size to be explored can be greatly expanded without increasing much computational cost.

Ab Initio Molecular Dynamics Study of Al, Ga and Si Adsorption on the Si(001) Surface

1993 ◽  
Vol 318 ◽  
Author(s):  
T. Yamasaki ◽  
M. Ikeda ◽  
Y. Morikawa ◽  
K. Terakura
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Density Of States ◽  
Local Density ◽  
Ab Initio Molecular Dynamics ◽  
Local Density Of States ◽  
Mono Layer ◽  
The Difference ◽  
Stable Structures

ABSTRACTThe adsorption of Al, Ga and Si on the Si(001) surface is studied by the ab initio molecular dynamics (Car-Parrinello) method based on the norm-conserving pseudopotential. In the stable structures obtained for half mono-layer coverage( ө = 1/2), these ad-atoms form dimers, but the dimer configurations are different. Al and Ga atoms form parallel dimers whose dimerization direction is parallel to that of substrate Si-dimers, while adsorbed Si atoms form (dense) orthogonal dimers. The electronic origin of the difference in the stable configurations among Al, Ga and Si ad-atoms is analyzed by calculating the local density of states (LDOS) of each atom.

Energetics and diffusion of liquid water and hydrated ions through nanopores in graphene: ab initio molecular dynamics simulation

2017 ◽  
Vol 19 (31) ◽  
pp. 20551-20558 ◽  
Author(s):  
Raúl Guerrero-Avilés ◽  
Walter Orellana
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Md Simulations ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Functional Theory ◽  
Hydrated Ions ◽  
And Diffusion

The energetics and diffusion of water molecules and hydrated ions (Na+, Cl−) passing through nanopores in graphene are addressed by dispersion-corrected density functional theory calculations and ab initio molecular dynamics (MD) simulations.

scholarly journals A Virtual Reality Ensemble Molecular Dynamics Workflow to Study Complex Conformational Changes in Proteins

2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Reality ◽  
Conformational Changes ◽  
Large Scale ◽  
A Priori ◽  
Computational Cost ◽  
Md Simulations ◽  
Collective Variables ◽  
Sampling Protocols ◽  
State Models

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>

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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