MLFF_QD

Machine Learning Force Fields for Quantum Dots platform.

Installation

Some packages are requiered for running the platform code. Below we explain what is requiered and how to install it.

Install SchNetPack with pip

The simplest way to install SchNetPack is through pip which will automatically get the source code from PyPI:

pip install schnetpack

Install SchNetPack from source

One can also install the most recent code from their repository:

git clone https://github.com/atomistic-machine-learning/schnetpack.git
cd schnetpack
pip install .

Visualization tools

SchNetPack supports multiple logging backends via PyTorch Lightning. The default logger is Tensorboard.

Getting started

The current version of the platform is developped for being run in a cluster. Thus, in this repository one can find the necessary code, a bash script example for submitting jobs in a slurm queue system and an input file example.

This plaform is currently being subject of several changes. Thus, on the meanwhile, descriptions of the files will be included here so they can be used.

Machine Learning Files

1. input_preparation.py: This file reads the xyz coordinates and constructs the npz files requiered for the training. Currently, it is not automatic, as it does not read the input.yaml and it has the name of the coordinates hardcoded.
2. xyztonpz.py: Used by input_preparation.py. It could be integrated there and a units conversion procedure will be included.
3. input.yaml: Example of an input file with the tuneable parameters.
4. training.py: The main training code. Currently is only based in SchNetPack. It should be redifined in a main function that calls the different packages. For that purpose, the functions should be organized in different files.
5. inference.py: It predicts the desired values after the training.
6. training_inference.sh: Example of a bash file for running both the training and the inference in a slurm based cluster. The input generation should be included here (or ideally in the python code).

Training locally

If an user wants to run locally the training code, one can do the following:

python training.py --config input_file.yaml

By default, if no input file is specified, the training code looks for a file called input.yaml.

Postprocessing

Currently, we have scripts used for preparaing the xyz files in the useful formats after DFT calculations with CP2K.