Next Article in Journal
Thin-Film Optical Devices Based on Transparent Conducting Oxides: Physical Mechanisms and Applications
Previous Article in Journal
Surface Anchoring Effects on the Formation of Two-Wavelength Surface Patterns in Chiral Liquid Crystals
Article Menu

Export Article

Open AccessArticle
Crystals 2019, 9(4), 191; https://doi.org/10.3390/cryst9040191

Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors

1
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
2
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208,USA
3
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guiyang 550025, China
*
Author to whom correspondence should be addressed.
Received: 9 March 2019 / Revised: 25 March 2019 / Accepted: 28 March 2019 / Published: 3 April 2019
(This article belongs to the Section Crystalline Materials)
PDF [742 KB, uploaded 3 April 2019]

Abstract

Computational prediction of crystal materials properties can help to do large-scale insilicon screening. Recent studies of material informatics have focused on expert design of multidimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features. Experiments showed that our method achieves better performance than conventional regression algorithms such as support vector machines and Random Forest. It is also better than CNN models using only the OFM features, the Magpie features, or the basic one-hot encodings. This demonstrates the advantages of CNN and feature fusion for materials property prediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the features extracted by the CNN to obtain greater understanding of the CNN-OFM model.
Keywords: material informatics; material descriptor; convolutional neural networks; features extraction; formation energy material informatics; material descriptor; convolutional neural networks; features extraction; formation energy
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Cao, Z.; Dan, Y.; Xiong, Z.; Niu, C.; Li, X.; Qian, S.; Hu, J. Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors. Crystals 2019, 9, 191.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Crystals EISSN 2073-4352 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top