https://github.com/zhubonan/disp
Distribute and manage crystal structure prediction workloads
https://github.com/zhubonan/disp
Last synced: about 1 month ago
JSON representation
Distribute and manage crystal structure prediction workloads
- Host: GitHub
- URL: https://github.com/zhubonan/disp
- Owner: zhubonan
- License: mit
- Created: 2022-02-23T19:23:55.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-08-07T14:26:59.000Z (10 months ago)
- Last Synced: 2025-03-30T10:12:37.325Z (2 months ago)
- Language: Python
- Homepage:
- Size: 1.88 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DISP (Distributed Structure Prediction)
Documentation: https://zhubonan.github.io/disp/
This package is for distributing the workload of *ab initio* random structure searching ([AIRSS](https://www.mtg.msm.cam.ac.uk/Codes/AIRSS)) over flexible computing resources.
With AIRSS, random yet physically sensible candidate crystal structures are generated and subsequently relaxed.
The search is inherently parallel and fast time-to-solution can be achieved by simultaneously running many "small" (a few tens of cores) DFT calculations in a highly efficient way.
In practice, the computing resources can be scattered spatially and temporally - there might be multiple machines that the search can run on, the each job may have to queue and subsequently run for only a limited amount of time.
DISP makes searching easier by providing a centralised server that handles distributing and controlling each search *worker*, getting real-time analytics data, as well as keeping a database of relaxed structures.
It is built upon the original AIRSS package and is fully compatible with the latter.
```mermaid
flowchart TD
A[PC] --> |search tasks| B[Server];
B --> |relaxed geometries| A;
subgraph HPC1
W1;
W2;
end
subgraph HPC
W3;
W4;
end
B --- W1[Worker1];
B --- W2[Worker2];
B --- W3[Worker3];
B --- W4[Worker4];
```
## Installation
A newer version of `pip` ( `>21`) might be needed - upgrade with:
```
pip install -U pip
```
install the package with:
``` none
pip install git+https://https://github.com/zhubonan/disp
```
## Requirements
- A MongoDB server is needed to act as the centralised workflow controller and a place to store search results.
- DISP and its python dependencies needs to be installed on both the local computer and the remote computing cluster/super computer, together with [AIRSS](https://www.mtg.msm.cam.ac.uk/Codes/AIRSS).
- Behind the scene, an extended version [fireworks](https://github.com/materialsproject/fireworks) is used for workflow management, it should be installed automatically with DISP.
- The plane wave DFT code [CASTEP](http://www.castep.org) is used for searching, although support for other code can be added if needed.
- Scheduler supported is limited to SGE and SLURM at the moment, but others can be added, given that job information is available at the run time.
## Usage
Usage is focused on command line, although python interface is also provided for analytics.
Deploy search with:
```
disp deploy search
```
Summary of throughput:
```
disp db throughput
```
Summary of finished/waiting jobs:
```
disp db summary
```
Pull completed search on to the disk:
```
disp db retrieve-project
```
## Other resources
- AIRSS code: https://www.mtg.msm.cam.ac.uk/Codes/AIRSS
- Documentation for AIRSS: https://airss-docs.github.io/
- metador - high throughput CASTEP geometry optimisation and analysis: https://github.com/ml-evs/matador
- aiida-fireworks-scheduler - for running standard AiiDA workflow alongside structure prediction workload from this package : https://github.com/zhubonan/aiida-fireworks-scheduler
## Reference
- Pickard, C. J.; Needs, R. J. High-Pressure Phases of Silane. Phys. Rev. Lett. 2006, 97 (4), 045504. https://doi.org/10.1103/PhysRevLett.97.045504.
- Pickard, C. J.; Needs, R. J. Ab Initio Random Structure Searching. Journal of physics. Condensed matter : an Institute of Physics journal 2011, 23 (5), 053201–053201. https://doi.org/10.1088/0953-8984/23/5/053201.