Data for "Topological grain boundary segregation transitions"

Cite as: Vivek Devulapalli et al. ,Topological grain boundary segregation transitions.Science386,420-424(2024). DOI:10.1126/science.adq4147

This repository contains the raw data from STEM imaging, EDS, and EELS experiments, the code used for GB simulations and theoretical calculations presented in the paper. 

=========================================================

MDMC-SGC directory contains the MD/MC simulation in the semi-grand-canonical
ensemble (Fig. 4 of the paper).

Fe-Ti-phase-diagram
===================

First, the bulk concentration of Fe in Ti is calculated as a function
of the chemical potential difference Δµ between Fe and Ti. This is
required to calculate the grain boundary excess over the bulk.

Here, it turns out that the bulk concentration is approximately zero
in the range of Δµ investigated.

MD/MC simulations of grain boundaries
=====================================

The following sample names map to the naming in the paper:

* ABC: Ti ground state structure
* large-1cage-2300000: isolated cage
* larger-2cages-3200000: double cage
* large-02-10000220: one layer of cages
* large-01-10000367: second layer of cages forming

Each directory contains subdirectories for all investigated Δµ. The
subdirectory `final-states` contains the final snapshots for each Δµ.

The script `prepare.py` was used to set up the simulations (template
for the LAMMPS input file is `lmp.in.template`). The script
`collect.py` was used to extract the thermodynamic excess properties
of the grain boundaries, stored in the file `T_0300K.excess.dat` in
each subdirectory.

The notebook `plot-excess.ipynb` can be used to plot the excess data.

=========================================================

# GRand canonical Interface Predictor (GRIP)

_Authors: [Enze Chen](https://enze-chen.github.io/) (Stanford University) and
[Timofey Frolov](https://people.llnl.gov/frolov2) (Lawrence Livermore National Laboratory)_     
_Version: 0.1.2024.01.21_

An algorithm for performing grand canonical optimization (GCO) of interfacial
structure (e.g., grain boundaries) in crystalline materials.
It automates sampling of slab translations and reconstructions
along with vacancy generation and finite temperature molecular dynamics (MD).
The algorithm repeatedly samples different structures in two phases:
  1. Structure generation and manipulation is largely handled using the
  [Atomic Simulation Environment (ASE)](https://wiki.fysik.dtu.dk/ase/).
  2. Molecular dynamics and static relaxations are currently performed using
  [LAMMPS](https://www.lammps.org), although in principle other energy
  evaluation methods (e.g., density functional theory in [VASP](https://www.vasp.at))
  may be used.

------

## Dependencies
- [Python](https://www.python.org/) (3.6+)
- [NumPy](https://numpy.org/) (1.23.0)
- [ASE](https://wiki.fysik.dtu.dk/ase/) (3.22.1)
- [LAMMPS](https://www.lammps.org) (stable)

_Optional_
- [pandas](https://pandas.pydata.org/) (1.5.3)
- [Matplotlib](https://matplotlib.org/stable/index.html) (3.5.3)

## Usage

Assuming the above libraries are installed, clone the repo and make the 
appropriate modifications in `params.yaml` (see file for detailed comments), 
including the path to the LAMMPS binary on your system.
If you wish, you can supply your own slabs for the bicrystal configuration as
POSCAR_LOWER and POSCAR_UPPER (in the [POSCAR](https://www.vasp.at/wiki/index.php/POSCAR)
file format).
Then call:
```python
python main.py
```
If you don't have LAMMPS or just want to test the script, you can run it with the `-d` flag.
See the `.examples` folder for a SLURM submission script for parallel execution (preferred).

## File structure
- `main.py`: Script to launch everything.
- `params.yaml`: Simulation parameters; **you'll want to edit this.**
- `core`: Main classes (`Bicrystal`, `Simulation`, etc.)
- `utility`: Main helper functions (`utils.py`, `unique.py`, etc.)
- `simul_files`: Files for simulations (LAMMPS input files, etc.)
- `best`: All relaxed structures are stored here. The naming convention is:
`lammps_Egb_n_X-SHIFT_Y-SHIFT_X-REPS_Y-REPS_TEMP_STEPS`

Duplicate files are periodically deleted by calling `clear_best()` in `utils/unique.py`.
The default method cleans about 1-3% of files on average.
Use the `-e` flag for more aggressive cleaning (>50%).
Use the `-s` flag to save the processed results to CSV from a pandas DataFrame.

Results can be visualized by running `utils/plot_gco.py` and it generates a GCO plot
of $E_{\mathrm{gb}}$ vs. $n$.
The `.examples` folder has this plot for several boundaries.
By default executing this file will save both the results (CSV) and the figure (PNG) 
to the same folder as the GRIP output files.

## Citation
If you use GRIP in your work, we would appreciate a citation to the original manuscript:

> Enze Chen, Tae Wook Heo, Brandon C. Wood, Mark Asta, and Timofey Frolov.
"Grand canonically optimized grain boundary phases in hexagonal close-packed titanium."
_arXiv:XXXX.YYYYY [cond-mat.mtrl-sci]_, 2024.

or in BibTeX format:

```
@article{chen_2024_grip,
    author = {Chen, Enze and Heo, Tae Wook and Wood, Brandon C. and Asta, Mark and Frolov, Timofey},
    title = {Grand canonically optimized grain boundary phases in hexagonal close-packed titanium},
    year = {2024},
    journal = {arXiv:XXXX.YYYYY [cond-mat.mtrl-sci]},
    doi = {10.48550/arXiv.XXXX.YYYYY},
}
```

=========================================================