Microstructural geometry revealed by NMR lineshape analysis
Data files
Dec 27, 2024 version files 13.44 MB
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cnt_viscg_320_20.dat
5.74 MB
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dump.comp.980000
1.20 MB
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generate_nanotube.lmp
1.89 KB
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in.visc_cnt
3.25 KB
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log.lammps
477.22 KB
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nanotube.data
2.24 MB
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paper3analysis.ipynb
3.46 MB
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README.md
8.21 KB
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submit_job.sh
708 B
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XeDiffCNT20A.mp4
308.47 KB
Abstract
We introduce a technique for extracting microstructural geometry from NMR lineshape analysis in porous materials at angstrom-scale resolution with the use of weak magnetic field gradients. Diverging from the generally held view of FID signals undergoing simple exponential decay, we show that a detailed analysis of the line shape can unravel structural geometry on much smaller scales than previously thought. While the original q-space PFG NMR relies on strong magnetic field gradients in order to achieve high spatial resolution, our current approach reaches comparable or higher resolution using much weaker gradients. As a model system, we simulated gas diffusion for xenon confined within carbon nanotubes over a range of temperatures and nanotube diameters in order to unveil manifestations of confinement in the diffusion behavior. We report a multiscale scheme that couples the above MD simulations with the generalized Langevin equation to estimate the transport properties of interest for this problem, such as diffusivity coefficients and NMR lineshapes, using the Green-Kubo correlation function to correctly evaluate time-dependent diffusion. Our results highlight how NMR methodologies can be adapted as effective means of structural investigation at very small scales when dealing with complicated geometries. This method is expected to find applications in materials science, catalysis, biomedicine, and other areas.
README: Microstructural geometry revealed by NMR lineshape analysis
https://doi.org/10.5061/dryad.80gb5mm0t
Description of the data and file structure
This dataset supports the analysis presented in the paper, “Microstructural Geometry Revealed by NMR Lineshape Analysis” (Niknam & Bouchard, https://arxiv.org/abs/2410.09700). The files include simulation input scripts, raw output data, logs, visual snapshots, and analysis notebooks for studying the molecular diffusion of xenon (Xe) gas inside confined carbon nanotubes (CNTs). A visualization movie of the diffusion process is also included to help interpret the results.
Contents
1. Simulation Input Files
Description: Input files for running LAMMPS molecular dynamics (MD) simulations.
Files:
in.visc_cnt: LAMMPS input script to compute viscosity and simulate particle dynamics in CNTs.
nanotube.data: Data file specifying the atomic configuration of the CNT structure.
generate_nanotube.lmp: Script for generating CNT configurations with specified radii and dimensions.
submit_job.sh: Example job submission script for running simulations on an HPC cluster.
2. Simulation Output Files
Description: Outputs generated during the LAMMPS simulations.
Files:
log.lammps: Contains detailed simulation logs, including energy outputs (kinetic, potential, total energy) and thermodynamic quantities at each time step.
dump.comp.980000: Snapshot of atomic positions and velocities at a specific timestep.
These frames can be used to visualize the molecular configuration of Xe atoms inside the CNT.
3. Visualization Files
Description: Visualization outputs illustrating the molecular diffusion of Xe gas in the CNT.
Files:
XeDiffCNT20A.mp4: A movie showing the diffusion of Xe atoms within a carbon nanotube (radius = 20 Å).
The movie was generated using snapshots (like dump.comp.980000) to provide a dynamic visualization of the simulation results.
4. Data Analysis
Description: Jupyter Notebook containing Python scripts for post-simulation analysis.
Files:
paper3analysis.ipynb: Calculates viscosity using Green-Kubo formalism from pressure tensor data.
Computes time- and frequency-dependent diffusion coefficients using numerical Laplace transforms.
Models NMR lineshapes using generalized Langevin equation (GLE)-based analysis.
Includes scripts for visualizing viscosity trends, diffusion coefficients, and NMR signal attenuation.
Simulation Details
1. System Configuration:
Carbon nanotubes (CNTs) of specified radii were constructed using generate_nanotube.lmp.
Xenon (Xe) gas particles were simulated within CNTs at densities consistent with experimental conditions.
2. Simulation Parameters:
Software: LAMMPS.
Interaction potentials: Lennard-Jones (LJ) potential for Xe-Xe and Xe-CNT interactions.
Time step: 1 femtosecond.
Total simulation time: 10 nanoseconds.
Thermodynamic data (energy, temperature, etc.) was logged every 1000 steps in log.lammps.
3. Visualization:
rames like dump.comp.980000 were used to visualize the molecular configurations.
The movie XeDiffCNT20A.mp4 illustrates the dynamic behavior of Xe atoms diffusing within the CNT.
Data Processing
1. Viscosity Calculation:
Green-Kubo formalism was applied to pressure tensor autocorrelation functions.
2. Diffusion Coefficient Calculation:
frequency-dependent diffusion coefficients were derived using the generalized Stokes-Einstein equation and numerical Laplace transforms.
3. NMR Lineshape Analysis:
GLE-based models were used to predict NMR signal attenuation and broadening effects in confined systems.
How to Use the Dataset
1. Run Simulations:
Use the input files (in.visc_cnt, nanotube.data, generate_nanotube.lmp) to reproduce molecular dynamics simulations in LAMMPS.
2. Analyze Results:
Open paper3analysis.ipynb in Jupyter Notebook.
Modify file paths to point to the raw data files (log.lammps, dump.comp.980000) and process the results.
3. Visualize Data:
Use tools like OVITO or VMD to visualize dump files (dump.comp.980000).
Play XeDiffCNT20A.mp4 to observe the molecular diffusion process.
4. Extend Analysis:
The provided scripts can be adapted to analyze different systems or geometries by modifying input files and parameters.
Citation
If you use this dataset or scripts in your work, please cite:
https://arxiv.org/abs/2410.09700 Niknam, M. & Bouchard, L.-S. (2024). Microstructural Geometry Revealed by NMR Lineshape Analysis.
Files and variables
File: submit_job.sh
Description: Example job submission script for running simulations on an HPC cluster.
File: in.visc_cnt
Description: LAMMPS input script to compute viscosity and simulate particle dynamics in CNTs.
File: generate_nanotube.lmp
Description: Script for generating CNT configurations with specified radii and dimensions.
File: paper3analysis.ipynb
Description: Jupyter Notebook containing Python scripts for post-simulation analysis.
File: log.lammps
Description: Contains detailed simulation logs, including energy outputs (kinetic, potential, total energy) and thermodynamic quantities at each time step.
File: dump.comp.980000
Description: Snapshot of atomic positions and velocities at a specific timestep.
File: nanotube.data
Description: Data file specifying the atomic configuration of the CNT structure.
File: cnt_viscg_320_20.dat
Description: An example of the simulation output, temp: 320K and CNT radius: 20 \AA
Code/software
1. Software for Viewing and Running Data
LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator)
Description: LAMMPS is an open-source molecular dynamics (MD) simulator widely used for modeling materials at the atomic scale.
Version: LAMMPS version 29 Oct 2020 was used for generating the simulation data.
Files:
Input scripts: in.visc_cnt, generate_nanotube.lmp, and nanotube.data.
Outputs: log.lammps (simulation log) and dump.comp.980000 (snapshots of atomic positions).
Workflow: LAMMPS runs the provided input files to simulate the molecular dynamics of xenon gas in CNTs. The generate_nanotube.lmp script generates CNT configurations, and in.visc_cnt runs simulations with output data stored in dump.comp.980000 and logs in log.lammps.
Download: LAMMPS Official Site
Jupyter Notebook (Python)
Description: Jupyter Notebook is an open-source interactive computing tool for running and documenting Python code.
Version: Jupyter Notebook version 6.4.12.
Key Packages:
numpy (v1.21.0): Numerical operations and array processing.
scipy (v1.7.0): Used for numerical Laplace and inverse Laplace transforms.
pandas (v1.3.0): For managing and processing tabular data.
matplotlib (v3.4.3): For visualizing viscosity trends, diffusion coefficients, and NMR lineshapes.
Files:
paper3analysis.ipynb: Jupyter Notebook that processes simulation outputs, computes transport properties, and generates plots.
Input files for analysis: cnt_viscg_320_20.dat, dump.comp.980000, and log.lammps.
Workflow:
1. Load MD simulation outputs (e.g., pressure tensor and atomic positions).
2. Compute viscosity using the Green-Kubo formalism.
3. Derive diffusion coefficients using Laplace transforms.
4. Predict NMR lineshapes using the generalized Langevin equation (GLE).
5. Visualize results using matplotlib.
Download: Jupyter Project
OVITO (Open Visualization Tool)
Description: OVITO is an open-source tool for visualizing and analyzing molecular dynamics simulation data.
Version: OVITO version 3.6.0.
Files:
dump.comp.980000: Atomic snapshots from the LAMMPS simulation.
Workflow: Use OVITO to load the dump files and visualize the molecular arrangement of xenon gas inside the CNT.
Access information
NA
Methods
Methods for Data Collection and Processing
1. Molecular Dynamics (MD) Simulations:
The primary data for this work were generated using Molecular Dynamics (MD) simulations conducted with the open-source software package LAMMPS. The following steps outline the simulation setup and execution:
System Configuration:
Carbon nanotubes (CNTs) with radii ranging from 8 Å to 40 Å were constructed using a cylindrical lattice arrangement of carbon atoms. Xenon (Xe) gas particles were introduced into the CNTs at densities consistent with experimental conditions. The number of Xe atoms was adjusted to maintain uniform gas density across all CNT geometries.
Interaction Potentials:
The interactions between Xe atoms were modeled using a Lennard-Jones (LJ) potential with parameters \epsilon = 1.77 \, \text{kJ/mol} and \sigma = 4.1 \, \text{Å} . Similarly, Xe-CNT interactions were described using an LJ potential with \epsilon = 0.71 \, \text{kJ/mol} and \sigma = 3.7 \, \text{Å}. The carbon atoms forming the CNT walls were held fixed during the simulation.
Simulation Parameters:
Simulations were performed under periodic boundary conditions along the CNT axis to mimic infinite tube lengths. The time step was set to 1 femtosecond, and each simulation was run for 10 nanoseconds to capture sufficient particle dynamics. Initial conditions, including velocities, were generated using a Maxwell-Boltzmann distribution corresponding to the specified temperatures (240–400 K).
Data Collection:
The Green-Kubo autocorrelation function for the pressure tensor components was computed directly from the MD simulations. These correlation functions provided the foundation for calculating time-dependent viscosity and diffusion coefficients. Position and velocity data for Xe particles were recorded at regular intervals to enable detailed analysis of molecular trajectories.
2. Data Processing and Analysis:
Viscosity Calculations:
The time-dependent viscosity of Xe gas inside CNTs was calculated using the Green-Kubo formalism. The pressure tensor autocorrelation function, sampled during the MD simulations, was integrated over time to determine the shear viscosity \eta(t). Frequency-dependent viscosity \eta(s) was subsequently obtained via a numerical Laplace transform.
Diffusion Coefficient Calculations:
Using the generalized Stokes-Einstein equation in the Laplace domain, the diffusion coefficient D(s) was computed as a function of frequency. An inverse Laplace transform was then applied to recover the time-dependent diffusion coefficient D(t). This approach ensured an accurate representation of memory effects and time-dependent transport properties.
NMR Lineshape Predictions:
The time-domain diffusion coefficients were incorporated into the generalized Langevin equation (GLE) framework to predict NMR lineshapes. By combining MD-derived parameters with the GLE, we successfully modeled signal attenuation and lineshape broadening as functions of molecular transport and confinement effects.
Validation and Sensitivity Analysis:
The results were validated by comparing simulated data to theoretical expectations and trends reported in the literature. Sensitivity analyses were conducted to ensure the robustness of the results with respect to simulation parameters, such as tube radius, temperature, and gas density.
3. Data Accessibility:
The database includes:
Raw MD Simulation Data:
• Positions and velocities of all Xe particles recorded during simulations.
• Pressure tensor components used to calculate Green-Kubo autocorrelation functions.
Processed Data Files:
• Time-dependent viscosity and diffusion coefficients derived from the Green-Kubo approach.
• Frequency- and time-dependent diffusion data used for NMR lineshape modeling.
Analysis Scripts:
• Python scripts for computing autocorrelation functions, viscosity, and diffusion coefficients.
• Numerical Laplace and inverse Laplace transform scripts for processing frequency-domain data.
• GLE-based NMR lineshape modeling code.