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Exciting advances have been made in artificial intelligence (AI) during thepast decades. Among them, applications of machine learning (ML) and deeplearning techniques brought human-competitive performances in various tasks offields, including image recognition, speech recognition and natural languageunderstanding. Even in Go, the ancient game of profound complexity, the AIplayer already beat human world champions convincingly with and withoutlearning from human. In this work, we show that our unsupervised machines(Atom2Vec) can learn the basic properties of atoms by themselves from theextensive database of known compounds and materials. These learned propertiesare represented in terms of high dimensional vectors, and clustering of atomsin vector space classifies them into meaningful groups in consistent with humanknowledge. We use the atom vectors as basic input units for neural networks andother ML models designed and trained to predict materials properties, whichdemonstrate significant accuracy.

Atom2Vec: learning atoms for materials discovery