Learning Representations of Inorganic Materials from Generative Adversarial Networks

Learning Representations of Inorganic Materials from Generative Adversarial Networks

The two most important aspects of material research using deep learning (DL) or machine learning (ML) are the characteristics of materials data and learning algorithms, where the proper characterization of materials data is essential for generating accurate models. At present, the characterization o...

Full description

Bibliographic Details
Main Authors: Tiantian Hu, Hui Song, Tao Jiang, Shaobo Li
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Symmetry
Subjects:
material characterization
GAN
deep learning
materials discovery
Online Access:https://www.mdpi.com/2073-8994/12/11/1889

Internet

https://www.mdpi.com/2073-8994/12/11/1889

Similar Items