  Exciting advances have been made in artificial intelligence (AI) during the
past decades. Among them, applications of machine learning (ML) and deep
learning techniques brought human-competitive performances in various tasks of
fields, including image recognition, speech recognition and natural language
understanding. Even in Go, the ancient game of profound complexity, the AI
player already beat human world champions convincingly with and without
learning from human. In this work, we show that our unsupervised machines
(Atom2Vec) can learn the basic properties of atoms by themselves from the
extensive database of known compounds and materials. These learned properties
are represented in terms of high dimensional vectors, and clustering of atoms
in vector space classifies them into meaningful groups in consistent with human
knowledge. We use the atom vectors as basic input units for neural networks and
other ML models designed and trained to predict materials properties, which
demonstrate significant accuracy.

Computational Physics;Materials Science