Learning atoms for materials discovery
Abstract
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. Furthermore, 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.
- Authors:
-
- Department of Physics, Stanford University, Stanford, CA 94305-4045,
- Department of Physics, Temple University, Philadelphia, PA 19122,
- Department of Physics, Stanford University, Stanford, CA 94305-4045,, Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025
- Publication Date:
- Research Org.:
- Energy Frontier Research Centers (EFRC) (United States). Center for Complex Materials from First Principles (CCM); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1457210
- Alternate Identifier(s):
- OSTI ID: 1463356
- Grant/Contract Number:
- AC02-76SF00515; SC0012575
- Resource Type:
- Published Article
- Journal Name:
- Proceedings of the National Academy of Sciences of the United States of America
- Additional Journal Information:
- Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 115 Journal Issue: 28; Journal ID: ISSN 0027-8424
- Publisher:
- Proceedings of the National Academy of Sciences
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 74 ATOMIC AND MOLECULAR PHYSICS; atomism; machine learning; materials discovery
Citation Formats
Zhou, Quan, Tang, Peizhe, Liu, Shenxiu, Pan, Jinbo, Yan, Qimin, and Zhang, Shou-Cheng. Learning atoms for materials discovery. United States: N. p., 2018.
Web. doi:10.1073/pnas.1801181115.
Zhou, Quan, Tang, Peizhe, Liu, Shenxiu, Pan, Jinbo, Yan, Qimin, & Zhang, Shou-Cheng. Learning atoms for materials discovery. United States. https://doi.org/10.1073/pnas.1801181115
Zhou, Quan, Tang, Peizhe, Liu, Shenxiu, Pan, Jinbo, Yan, Qimin, and Zhang, Shou-Cheng. Tue .
"Learning atoms for materials discovery". United States. https://doi.org/10.1073/pnas.1801181115.
@article{osti_1457210,
title = {Learning atoms for materials discovery},
author = {Zhou, Quan and Tang, Peizhe and Liu, Shenxiu and Pan, Jinbo and Yan, Qimin and Zhang, Shou-Cheng},
abstractNote = {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. Furthermore, 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.},
doi = {10.1073/pnas.1801181115},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 28,
volume = 115,
place = {United States},
year = {Tue Jun 26 00:00:00 EDT 2018},
month = {Tue Jun 26 00:00:00 EDT 2018}
}
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https://doi.org/10.1073/pnas.1801181115
https://doi.org/10.1073/pnas.1801181115
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Cited by: 106 works
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