Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 115, Issue 28, Pages E6411-E6417Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1801181115
Keywords
atomism; machine learning; materials discovery
Categories
Funding
- Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering [DE-AC02-76SF00515]
- Center for the Computational Design of Functional Layered Materials, an Energy Frontier Research Center - US Department of Energy, Office of Science, Basic Energy Sciences [DE-SC0012575]
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Exciting advances have been made in artificial intelligence (AI) during recent 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 has already beat human world champions convincingly with and without learning from the 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 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.
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