vsgd / atom2vec

Atom2Vec: a simple way to describe atoms for machine learning

Atom2Vec

A python implement of Atom2Vec: a simple way to describe atoms for machine learning

(Updated 06/21/2021: We refactored the code with pymatgen, you can find old version in branch old_version. Now the code is fully typed and tested.)

Background

Atom2Vec is first proposed on Zhou Q, Tang P, Liu S, et al. Learning atoms for materials discovery[J]. Proceedings of the National Academy of Sciences, 2018, 115(28): E6411-E6417.

Demo

Installation

pip install atom2vec

Usage

Generating atom vectors and atom similarity matrix

We use pymatgen.core.Structure to store all the structures.

from atom2vec import AtomSimilarity
from pymatgen.core import Structure
from typing import List

structures: List[Structure]
atom_similarity = AtomSimilarity.from_structures(structures, 
                                                 k_dim=100, max_elements=3)

Query atom vectors

from atom2vec import AtomSimilarity
from pymatgen.core import Element
from typing import List

atom_similarity: AtomSimilarity
atom_vector: List[float]

atom_vector = atom_similarity.get_atom_vector(1)  # atomic index
atom_vector = atom_similarity.get_atom_vector("H")  # atom's name
atom_vector = atom_similarity.get_atom_vector(Element("H"))  # pymatgen Element Enum

Query atom similarity

from atom2vec import AtomSimilarity
from pymatgen.core import Element

atom_similarity: AtomSimilarity
similarity: float

similarity = atom_similarity["Ca", "Sr"]