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Title: 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:
; ; ; ; ;
Publication Date:
Research Org.:
Energy Frontier Research Centers (EFRC) (United States). Center for Complex Materials from First Principles (CCM); SLAC National Accelerator Lab., 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. doi: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. doi: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 = {2018},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1073/pnas.1801181115

Citation Metrics:
Cited by: 18 works
Citation information provided by
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