A general and transferable deep learning framework for predicting phase formation in materials

Abstract Machine learning has been widely exploited in developing new materials. However, challenges still exist: small dataset is common for most tasks; new datasets, special descriptors and specific models need to be built from scratch when facing a new task; knowledge cannot be readily transferre...

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Autores principales:	Shuo Feng, Huadong Fu, Huiyu Zhou, Yuan Wu, Zhaoping Lu, Hongbiao Dong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/cb9c16f84ac54c0ca80356bf9df48132
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spelling	oai:doaj.org-article:cb9c16f84ac54c0ca80356bf9df481322021-12-02T10:48:30ZA general and transferable deep learning framework for predicting phase formation in materials10.1038/s41524-020-00488-z2057-3960https://doaj.org/article/cb9c16f84ac54c0ca80356bf9df481322021-01-01T00:00:00Zhttps://doi.org/10.1038/s41524-020-00488-zhttps://doaj.org/toc/2057-3960Abstract Machine learning has been widely exploited in developing new materials. However, challenges still exist: small dataset is common for most tasks; new datasets, special descriptors and specific models need to be built from scratch when facing a new task; knowledge cannot be readily transferred between independent models. In this paper we propose a general and transferable deep learning (GTDL) framework for predicting phase formation in materials. The proposed GTDL framework maps raw data to pseudo-images with some special 2-D structure, e.g., periodic table, automatically extracts features and gains knowledge through convolutional neural network, and then transfers knowledge by sharing features extractors between models. Application of the GTDL framework in case studies on glass-forming ability and high-entropy alloys show that the GTDL framework for glass-forming ability outperformed previous models and can correctly predicted the newly reported amorphous alloy systems; for high-entropy alloys the GTDL framework can discriminate five types phases (BCC, FCC, HCP, amorphous, mixture) with accuracy and recall above 94% in fivefold cross-validation. In addition, periodic table knowledge embedded in data representations and knowledge shared between models is beneficial for tasks with small dataset. This method can be easily applied to new materials development with small dataset by reusing well-trained models for related materials.Shuo FengHuadong FuHuiyu ZhouYuan WuZhaoping LuHongbiao DongNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Shuo Feng Huadong Fu Huiyu Zhou Yuan Wu Zhaoping Lu Hongbiao Dong A general and transferable deep learning framework for predicting phase formation in materials
description	Abstract Machine learning has been widely exploited in developing new materials. However, challenges still exist: small dataset is common for most tasks; new datasets, special descriptors and specific models need to be built from scratch when facing a new task; knowledge cannot be readily transferred between independent models. In this paper we propose a general and transferable deep learning (GTDL) framework for predicting phase formation in materials. The proposed GTDL framework maps raw data to pseudo-images with some special 2-D structure, e.g., periodic table, automatically extracts features and gains knowledge through convolutional neural network, and then transfers knowledge by sharing features extractors between models. Application of the GTDL framework in case studies on glass-forming ability and high-entropy alloys show that the GTDL framework for glass-forming ability outperformed previous models and can correctly predicted the newly reported amorphous alloy systems; for high-entropy alloys the GTDL framework can discriminate five types phases (BCC, FCC, HCP, amorphous, mixture) with accuracy and recall above 94% in fivefold cross-validation. In addition, periodic table knowledge embedded in data representations and knowledge shared between models is beneficial for tasks with small dataset. This method can be easily applied to new materials development with small dataset by reusing well-trained models for related materials.
format	article
author	Shuo Feng Huadong Fu Huiyu Zhou Yuan Wu Zhaoping Lu Hongbiao Dong
author_facet	Shuo Feng Huadong Fu Huiyu Zhou Yuan Wu Zhaoping Lu Hongbiao Dong
author_sort	Shuo Feng
title	A general and transferable deep learning framework for predicting phase formation in materials
title_short	A general and transferable deep learning framework for predicting phase formation in materials
title_full	A general and transferable deep learning framework for predicting phase formation in materials
title_fullStr	A general and transferable deep learning framework for predicting phase formation in materials
title_full_unstemmed	A general and transferable deep learning framework for predicting phase formation in materials
title_sort	general and transferable deep learning framework for predicting phase formation in materials
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/cb9c16f84ac54c0ca80356bf9df48132
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A general and transferable deep learning framework for predicting phase formation in materials

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