https://github.com/materialsproject/mpmorph
MPmorph is a collection of tools to run and analyze ab-initio molecular dynamics (AIMD) calculations run with VASP, and is currently under development. It relies heavily on tools developed by the Materials Project (pymatgen, custodian, fireworks) and atomate.
https://github.com/materialsproject/mpmorph
Last synced: 26 days ago
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MPmorph is a collection of tools to run and analyze ab-initio molecular dynamics (AIMD) calculations run with VASP, and is currently under development. It relies heavily on tools developed by the Materials Project (pymatgen, custodian, fireworks) and atomate.
- Host: GitHub
- URL: https://github.com/materialsproject/mpmorph
- Owner: materialsproject
- License: other
- Created: 2016-03-25T19:39:09.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2024-02-28T23:09:53.000Z (about 1 year ago)
- Last Synced: 2024-06-19T00:20:48.909Z (11 months ago)
- Language: Python
- Homepage:
- Size: 9.32 MB
- Stars: 60
- Watchers: 12
- Forks: 29
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# MPmorph
MPmorph is a collection of tools to run and analyze ab-initio molecular dynamics (AIMD) calculations run with VASP,
and is currently under development.
It relies heavily on tools developed by the Materials Project ([pymatgen](http://www.pymatgen.org),
[custodian](https://github.com/materialsproject/custodian),
[fireworks](https://github.com/materialsproject/fireworks)) and [atomate](https://github.com/hackingmaterials/atomate).
MPmorph provides:
* Infrastructure for dynamic VASP MD workflows:
* Tools to create dynamic MD workflows using atomate
* E.g. generation of new MD runs based on the given criterion (currently pressure, for liquid/amorphous phase density estimation)
* Tools for statistical analysis of:
* Static observables:
* Radial distribution functions
* Coordination numbers
* Voronoi analysis
* Polyhedra connectivities
* Thermodynamic quantities
* Diffusion coefficients:
* Robust calculation of diffusion coefficients (D) and activation energies (Q).
* Rigorous error analysis for D and Q.
# Installation
Before installing mpmorph, install the latest version of [pymatgen](http://www.pymatgen.org),
[custodian](https://github.com/materialsproject/custodian),
[fireworks](https://github.com/materialsproject/fireworks)) and [atomate](https://github.com/hackingmaterials/atomate)
clone the repository to your computer and install using
```bash
python setup.py develop
```
If you wish to make amorphous structures, please install [packmol](http://m3g.iqm.unicamp.br/packmol/home.shtml) on your machine and add the following line to your bash profile.
```bash
export PACKMOL_PATH="path_to_packmol_executable_here"
```
# Using MPmorph
Before diving headfirst into mpmorph, one should get familiar with how to run fireworks before jumping into mpmorph. Relevant features of fireworks include setting up a fireworks database, adding workflows to the database, configuring a conda environment for running fireworks on a supercomputer, launching jobs to a workload manager (using qlaunch), and monitoring fireworks jobs. For a quick tutorial, check out [beginner](https://www.youtube.com/watch?v=-MalOMJt34U) and [intermediate](https://www.youtube.com/watch?v=zYA_BbKwVO4) atomate/fireworks tutorials from our 2019 Materials project workshop.
A sample of using mpmorph to run an AIMD simulation at 1500K for 200ps (100k steps at 2fs/step) is shown below:
```python
from mpmorph.workflows.converge import get_converge_wf
from pymatgen.ext.matproj import MPRester
from fireworks import LaunchPad
mpr = MPRester()
structure = mpr.get_structure_by_material_id('mp-1143')
structure.make_supercell([3, 3, 3])
wf = get_converge_wf(structure, temperature = 750, target_steps = 100000)
lp = LaunchPad.auto_load()
lp.add_wf(wf)
```
To generate an amorphous structure, run the following code:
```python
from mpmorph.runners.amorphous_maker import get_random_packed
from mpmorph.workflows.converge import get_converge_wf
from fireworks import LaunchPad
structure = get_random_packed('Li', target_atoms=100)
wf = get_converge_wf(structure, temperature = 5000, target_steps = 10000)
lp = LaunchPad.auto_load()
lp.add_wf(wf)
```
## Customizing runs
At the simplest level of customizing the workflow, one can change the temperature, total number of steps by changing the args passed to get_converge_wf().
For more advanced changes, pass a dictionary for "converge_args", "spawner_args", or "prod_args". This allows customization of vasp inputs, starting and ending temperatures, number of steps, and convergence crieteria.
### Examples:
Changing the rescale parameter, to make for larger volume changes at each stage:
```python
from mpmorph.runners.amorphous_maker import get_random_packed
from mpmorph.workflows.converge import get_converge_wf
from fireworks import LaunchPad
structure = get_random_packed('Li', target_atoms=100)
spawner_args = {'rescale_params': {'beta': 5e-6}}
wf = get_converge_wf(structure, temperature = 5000, target_steps = 10000, spawner_args=spawner_args)
lp = LaunchPad.auto_load()
lp.add_wf(wf)
```
Changing precision of production runs to PREC=Low:
```python
from mpmorph.runners.amorphous_maker import get_random_packed
from mpmorph.workflows.converge import get_converge_wf
from fireworks import LaunchPad
structure = get_random_packed('Li', target_atoms=100)
prod_args = {'optional_fw_params': {'override_default_vasp_params': {'user_incar_settings': {'PREC': 'Normal'}}}}
wf = get_converge_wf(structure, temperature = 5000, target_steps = 10000, prod_args = prod_args)
lp = LaunchPad.auto_load()
lp.add_wf(wf)
```
# Citation
If you use mpmorph, please cite the following papers:
* Aykol, M., Dwaraknath, S.S., Sun, W. and Persson, K.A., 2018. Thermodynamic limit for synthesis of metastable inorganic materials. Science advances, 4(4), p.eaaq0148.
* Aykol, M. and Persson, K.A., 2018. Oxidation Protection with Amorphous Surface Oxides: Thermodynamic Insights from Ab Initio Simulations on Aluminum. ACS applied materials & interfaces, 10(3), pp.3039-3045.