Introduction

Data is central in Machine Learning and ML4Chem provides some tools to prepare your Datas. We support the following input formats:

1. Atomic Simulation Environment (ASE).

We will be adding support to other libraries, soon.

Data

The ml4chem.data.handler module allows users to adapt data to the right format to inter-operate with any other module of Ml4Chem.

Its usage is very simple:

from ml4chem.data.handler import Data
from ase.io import Trajectory

images = Trajectory("images.traj")
data_handler = Data(images, purpose="training")
traing_set, targets = data_handler.get_data(purpose="training")

In the example above, an ASE trajectory file is loaded into memory and passed as an argument to instantiate the Data class with purpose="training". The .get_images() class method returns a hashed dictionary with the molecules in images.traj and the targets variable as a list of energies.

For more information please refer to ml4chem.data.handler.