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

Shuo Feng, Huadong Fu, Huiyu Zhou, Yuan Wu, Zhaoping Lu, Hongbiao Dong*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
4 Downloads (Pure)

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.

Original languageEnglish
Article number10
Number of pages10
Journalnpj Computational Materials
Volume7
DOIs
Publication statusPublished - 25 Jan 2021
Externally publishedYes

Bibliographical note

Funding Information:
S.F. wishes to acknowledge EPSRC CDT (Grant No: EP/L016206/1) in Innovative Metal Processing for providing a Ph.D. studentship for this study.

Publisher Copyright:
© 2021, The Author(s).

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

  • Modelling and Simulation
  • Materials Science(all)
  • Mechanics of Materials
  • Computer Science Applications

Fingerprint Dive into the research topics of 'A general and transferable deep learning framework for predicting phase formation in materials'. Together they form a unique fingerprint.

Cite this