ElementEmbeddings 0.1

Element Embeddings

ElementEmbeddings

The Element Embeddings package provides high-level tools for analysing elemental embeddings data. This primarily involves visualising the correlation between embedding schemes using different statistical measures.

Motivation

Machine learning approaches for materials informatics have become increasingly widespread. Some of these involve the use of deep learning techniques where the representation of the elements is learned rather than specified by the user of the model. While an important goal of machine learning training is to minimise the chosen error function to make more accurate predictions, it is also important for us material scientists to be able to interpret these models. As such, we aim to evaluate and compare different atomic embedding schemes in a consistent framework.

Getting started

ElementEmbeddings's main feature, the Embedding class is accessible by importing the class.

Installation

The latest version can be installed using:

pip install git+git://github.com/WMD-group/ElementEmbeddings.git

For development, you can clone the repository and install the package in editable mode. To clone the repository and make a local installation, run the following commands:

git clone https://github.com/WMD-group/ElementEmbeddings.git
cd ElementEmbeddings
pip install --user -e .

With -e pip will create links to the source folder so that changes to the code will be immediately reflected on the PATH.

Usage

For simple usage, you can instantiate an Embedding object using one of the embeddings in the data directory. For this example, let's use the magpie elemental representation.

# Import the class
>>> from elementembeddings.core import Embedding

# Load the magpie data
>>> magpie = Embedding.load_data('magpie')

We can access some of the properties of the Embedding class. For example, we can find the dimensions of the elemental representation and the list of elements for which an embedding exists.

# Print out some of the properties of the ElementEmbeddings class
>>> print(f'The magpie representation has embeddings of dimension {magpie.dim}') 
>>> print(magpie.element_list) # prints out all the elements considered for this representation
The magpie representation has embeddings of dimension 22
['H', 'He', 'Li', 'Be', 'B', 'C', 'N', 'O', 'F', 'Ne', 'Na', 'Mg', 'Al', 'Si', 'P', 'S', 'Cl', 'Ar', 'K', 'Ca', 'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn', 'Ga', 'Ge', 'As', 'Se', 'Br', 'Kr', 'Rb', 'Sr', 'Y', 'Zr', 'Nb', 'Mo', 'Tc', 'Ru', 'Rh', 'Pd', 'Ag', 'Cd', 'In', 'Sn', 'Sb', 'Te', 'I', 'Xe', 'Cs', 'Ba', 'La', 'Ce', 'Pr', 'Nd', 'Pm', 'Sm', 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Ta', 'W', 'Re', 'Os', 'Ir', 'Pt', 'Au', 'Hg', 'Tl', 'Pb', 'Bi', 'Po', 'At', 'Rn', 'Fr', 'Ra', 'Ac', 'Th', 'Pa', 'U', 'Np', 'Pu', 'Am', 'Cm', 'Bk']