Fix README.org
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佐久間 美波
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#+TITLE: Li2S-P2S5-LiI Glass: PS4-Centered Ion Density Analysis
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#+TITLE: PS₄³⁻-Centered Li⁺/I⁻ Spatial Density Analysis in Li₂S–P₂S₅–LiI Glasses
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#+AUTHOR: Minami Sakuma
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#+OPTIONS: toc:2 num:nil
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* Overview
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This repository contains Python scripts for analyzing and visualizing
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Li and I spatial probability distributions around =PS4^{3-}= units in
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Li2S-P2S5-LiI glass trajectories.
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This repository provides Python scripts for analyzing the spatial
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distributions of Li⁺ and I⁻ around PS₄³⁻ tetrahedra in
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Li₂S–P₂S₅–LiI glass trajectories.
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The workflow consists of two steps:
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The workflow aligns individual PS₄³⁻ units into a common reference frame,
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accumulates the positions of nearby Li⁺ ions and I⁻ ions, and exports
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three-dimensional spatial-density fields in Gaussian cube format.
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1. =dump2cube.py=
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- Reads a LAMMPS trajectory.
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- Aligns each =PS4^{3-}= tetrahedron using the nearest I^- ion.
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- Accumulates Li and I positions in the aligned coordinate system.
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- Outputs three-dimensional probability-density data in Gaussian cube format.
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The generated cube files can be visualized as isosurfaces using Mayavi.
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2. =cube2mayavi.py=
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- Reads the generated cube file.
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- Visualizes the spatial probability density as an isosurface using Mayavi.
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- Displays the reference =PS4^{3-}= tetrahedron.
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* Workflow
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The scripts are intended to analyze the local geometrical relationship
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between I^- ions and =PS4^{3-}= units in Li2S-P2S5-LiI glasses.
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#+begin_example
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LAMMPS trajectory
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v
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dump2cube.py
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+-- PS₄_Li.cube : Li⁺ spatial-density field
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+-- PS₄_I.cube : I⁻ spatial-density field
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v
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cube2mayavi.py
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v
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3D isosurface visualization
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#+end_example
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* Files
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| File | Description |
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|---+---|
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| =dump2cube.py= | Converts a LAMMPS trajectory into Li/I probability-density cube files. |
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| =cube2mayavi.py= | Visualizes a cube file with Mayavi. |
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| =050Li3PS4-050LiI.lammpstrj= | Example LAMMPS trajectory. |
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| =050Li3PS4-050LiI_PS4_I.cube= | Example I^- probability-density cube file. |
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| =dump2cube.py= | Reads a LAMMPS trajectory, aligns PS₄³⁻ units, and generates Li⁺/I⁻ cube files. |
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| =cube2mayavi.py= | Reads a Gaussian cube file and visualizes the density field using Mayavi. |
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| =050Li3PS4-050LiI.lammpstrj= | Example LAMMPS trajectory for a Li₂S–P₂S₅–LiI glass. |
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| =050Li3PS4-050LiI_PS4_I.cube= | Example cube file containing the I⁻ spatial-density field. |
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* Requirements
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** Python packages
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The scripts require Python 3 and the following packages:
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- NumPy
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- Mayavi
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- VTK
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- Traits
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- PyQt5 or PySide6, depending on the Mayavi installation
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- PyQt5 or PySide6
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Example installation using conda:
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Mayavi is most easily installed through conda-forge.
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#+begin_src bash
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conda create -n ps4-density python=3.10
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@@ -51,49 +62,61 @@ conda activate ps4-density
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conda install -c conda-forge numpy mayavi pyqt
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#+end_src
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Alternatively, NumPy can be installed using pip:
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If NumPy is not installed, it can also be installed with pip.
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#+begin_src bash
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pip install numpy
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#+end_src
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Mayavi installation is generally more stable with conda-forge.
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* Input Trajectory Format
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=dump2cube.py= assumes a LAMMPS trajectory containing the following
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atom columns:
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=dump2cube.py= expects a LAMMPS trajectory containing the following atom
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columns:
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#+begin_example
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ITEM: ATOMS id type element mol x y z
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#+end_example
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The trajectory must contain at least the following elements:
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The trajectory must contain the following elements:
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- Li
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- P
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- S
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- I
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The script assumes that P and S atoms belonging to the same
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=PS4^{3-}= unit share the same molecule ID (=mol=).
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The script assumes that P and S atoms belonging to the same PS₄³⁻ unit
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share the same molecule ID (=mol=).
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* Analysis Procedure
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The current workflow has been tested primarily for orthorhombic
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simulation cells. Periodic boundary conditions are applied when
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calculating relative atomic positions.
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For each trajectory frame:
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* Analysis Method
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1. Each P atom is selected as the center of a reference =PS4^{3-}= unit.
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2. The four nearest I^- ions are identified.
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3. The nearest I^- ion is used to define the orientation of the =PS4^{3-}= unit.
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4. The =PS4^{3-}= tetrahedron is rotated into a common reference frame.
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5. Li positions within the cutoff distance are accumulated.
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6. The positions of the four nearest I^- ions are accumulated.
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7. Three-dimensional histograms are written as cube files.
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For every frame in the trajectory, the following procedure is performed.
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The reference orientation is defined as follows:
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1. Each P atom is selected as the center of a reference PS₄³⁻ tetrahedron.
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2. The four nearest I⁻ ions around the selected P atom are identified.
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3. The nearest I⁻ ion is used to define the orientation of the PS₄³⁻ unit.
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4. The four S atoms belonging to the PS₄³⁻ unit are rotated into a common
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reference coordinate system.
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5. Li⁺ ions within the specified cutoff distance from P are collected.
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6. The four nearest I⁻ ions are collected.
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7. The accumulated Li⁺ and I⁻ positions are converted into
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three-dimensional histograms.
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8. The histograms are exported in Gaussian cube format.
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- The S atom farthest from the nearest I^- ion is aligned with the z axis.
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- A second S atom is used to fix the rotation around the z axis.
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** Definition of the reference orientation
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The PS₄³⁻ tetrahedron is aligned using the nearest I⁻ ion.
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- The S atom farthest from the nearest I⁻ ion is aligned with the z axis.
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- A second S atom is used to define the rotation around the z axis.
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- The P atom is placed at the origin.
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This alignment enables the spatial distributions of Li⁺ and I⁻ around
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many PS₄³⁻ units and trajectory frames to be accumulated in a common
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coordinate system.
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* Usage
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@@ -111,17 +134,17 @@ Arguments:
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| Argument | Description |
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|---+---|
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| =-i=, =--trjfile= | Input LAMMPS trajectory file. |
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| =-m=, =--mesh= | Number of grid points in x, y, and z directions. |
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| =-cut=, =--cutoff= | Spatial cutoff radius in angstrom. |
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| =-m=, =--mesh= | Number of grid points along the x, y, and z directions. |
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| =-cut=, =--cutoff= | Cutoff distance around the reference P atom in Å. |
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Expected output files:
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For the example above, the expected output files are:
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#+begin_example
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050Li3PS4-050LiI_PS4_Li.cube
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050Li3PS4-050LiI_PS4_I.cube
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#+end_example
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** Visualize I^- Probability Density
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** Visualize the I⁻ Spatial-Density Field
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#+begin_src bash
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python cube2mayavi.py \
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@@ -130,7 +153,7 @@ python cube2mayavi.py \
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-iso 1.26483e-09
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#+end_src
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** Visualize Li+ Probability Density
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** Visualize the Li⁺ Spatial-Density Field
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#+begin_src bash
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python cube2mayavi.py \
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@@ -139,56 +162,76 @@ python cube2mayavi.py \
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-iso 1.0e-09
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#+end_src
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The appropriate isovalue depends on the trajectory length, mesh size,
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cutoff radius, and probability-density distribution. It should therefore
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be adjusted for each dataset.
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The appropriate isovalue depends on the number of trajectory frames,
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the number of PS₄³⁻ units, the mesh size, and the cutoff distance.
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Therefore, the =-iso= value should be adjusted for each dataset.
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* Output
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The cube files contain:
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Each generated cube file contains:
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- A reference =PS4^{3-}= tetrahedron:
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- P atom at the origin
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- A reference PS₄³⁻ tetrahedron
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- One P atom located at the origin
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- Four S atoms in the aligned coordinate system
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- A three-dimensional spatial probability-density field for Li or I
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- A three-dimensional spatial-density field for Li⁺ or I⁻
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The cube files can be visualized using:
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The cube files can also be viewed with software supporting Gaussian cube
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format, including:
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- Mayavi
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- VMD
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- PyMOL
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- ParaView
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- Other software supporting Gaussian cube files
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- PyMOL
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* Visualization Colors
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* Visualization Settings
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The default visualization settings in =cube2mayavi.py= are:
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The default colors in =cube2mayavi.py= are:
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| Object | Color |
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|---+---|
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| P | Purple |
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| S | Yellow |
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| Li probability density | Blue |
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| I probability density | Red |
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| P-S bonds | Gray |
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| P atom | Purple |
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| S atom | Yellow |
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| P–S bond | Gray |
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| Li⁺ density | Blue |
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| I⁻ density | Red |
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* Notes
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For Li⁺ visualization, the script separates the density field into
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an inner region and an outer region:
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- The trajectory is treated using periodic boundary conditions.
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- Coordinates are converted to fractional coordinates before alignment.
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- The current implementation assumes an orthorhombic simulation box for
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the trajectory parsing procedure.
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- The script includes a triclinic-cell lattice conversion function, but
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the exact input format should be checked before applying it to
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triclinic LAMMPS trajectories.
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- Large trajectory and cube files can exceed the standard GitHub file-size
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limit. Git LFS is recommended when files are larger than 100 MB.
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- Inner region: within 5 Å of the P atom
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- Outer region: between 5 Å and 9 Å from the P atom
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* Example Interpretation
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The I⁻ cube file can be used to examine whether I⁻ ions are distributed
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uniformly around PS₄³⁻ tetrahedra or occupy preferred directions.
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An anisotropic I⁻ density distribution indicates that I⁻ ions form
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characteristic local geometries relative to PS₄³⁻ units rather than being
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randomly distributed in the glass network.
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* Notes and Limitations
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- The input trajectory must include =id type element mol x y z= columns.
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- The P and S atoms of a PS₄³⁻ tetrahedron must have the same molecule ID.
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- The cube field is generated from accumulated histogram counts and is
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intended for relative spatial-distribution analysis.
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- The output file name is generated from the input trajectory name.
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The current implementation expects a filename containing =LiI=.
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- Large trajectory and cube files may exceed GitHub's 100 MB file-size
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limit. Git LFS is recommended for large files.
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- The visualization script opens an interactive Mayavi window. A GUI-capable
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Python environment is required.
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- Before use, confirm that the trajectory format and simulation-cell
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definition are compatible with the parser implemented in =dump2cube.py=.
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* Citation
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If this repository is used in research, please cite the corresponding
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publication or presentation describing the Li2S-P2S5-LiI glass analysis.
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If this repository contributes to published work, please cite the
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corresponding paper, poster, presentation, or dataset describing the
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Li₂S–P₂S₅–LiI glass simulations and PS₄³⁻-centered spatial-density
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analysis.
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* License
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This repository is intended for academic research use.
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This repository is intended for academic and research use.
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