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Universal fragment descriptors for predicting properties of inorganic crystals
Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is c...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5465371/ https://www.ncbi.nlm.nih.gov/pubmed/28580961 http://dx.doi.org/10.1038/ncomms15679 |
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author | Isayev, Olexandr Oses, Corey Toher, Cormac Gossett, Eric Curtarolo, Stefano Tropsha, Alexander |
author_facet | Isayev, Olexandr Oses, Corey Toher, Cormac Gossett, Eric Curtarolo, Stefano Tropsha, Alexander |
author_sort | Isayev, Olexandr |
collection | PubMed |
description | Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules. |
format | Online Article Text |
id | pubmed-5465371 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-54653712017-06-22 Universal fragment descriptors for predicting properties of inorganic crystals Isayev, Olexandr Oses, Corey Toher, Cormac Gossett, Eric Curtarolo, Stefano Tropsha, Alexander Nat Commun Article Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules. Nature Publishing Group 2017-06-05 /pmc/articles/PMC5465371/ /pubmed/28580961 http://dx.doi.org/10.1038/ncomms15679 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Isayev, Olexandr Oses, Corey Toher, Cormac Gossett, Eric Curtarolo, Stefano Tropsha, Alexander Universal fragment descriptors for predicting properties of inorganic crystals |
title | Universal fragment descriptors for predicting properties of inorganic crystals |
title_full | Universal fragment descriptors for predicting properties of inorganic crystals |
title_fullStr | Universal fragment descriptors for predicting properties of inorganic crystals |
title_full_unstemmed | Universal fragment descriptors for predicting properties of inorganic crystals |
title_short | Universal fragment descriptors for predicting properties of inorganic crystals |
title_sort | universal fragment descriptors for predicting properties of inorganic crystals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5465371/ https://www.ncbi.nlm.nih.gov/pubmed/28580961 http://dx.doi.org/10.1038/ncomms15679 |
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