View source for 18091877. PEROVSKITE SYNTHESIZABILITY PREDICTION METHOD USING GRAPH CONVOLUTIONAL NEURAL NETWORKS AND POSITIVE UNLABELED LEARNING simplified abstract (Korea Advanced Institute of Science and Technology)

=PEROVSKITE SYNTHESIZABILITY PREDICTION METHOD USING GRAPH CONVOLUTIONAL NEURAL NETWORKS AND POSITIVE UNLABELED LEARNING=


==Organization Name==

[[:Category:Korea Advanced Institute of Science and Technology|Korea Advanced Institute of Science and Technology]]


[[Category:Korea Advanced Institute of Science and Technology]]

==Inventor(s)==

[[:Category:You Sung Jung of Daejeon (KR)|You Sung Jung of Daejeon (KR)]][[Category:You Sung Jung of Daejeon (KR)]]

[[:Category:Geun Ho Gu of Daejeon (KR)|Geun Ho Gu of Daejeon (KR)]][[Category:Geun Ho Gu of Daejeon (KR)]]

[[:Category:Ju Hwan Noh of Daejeon (KR)|Ju Hwan Noh of Daejeon (KR)]][[Category:Ju Hwan Noh of Daejeon (KR)]]

[[:Category:Ji Don Jang of Daejeon (KR)|Ji Don Jang of Daejeon (KR)]][[Category:Ji Don Jang of Daejeon (KR)]]

==PEROVSKITE SYNTHESIZABILITY PREDICTION METHOD USING GRAPH CONVOLUTIONAL NEURAL NETWORKS AND POSITIVE UNLABELED LEARNING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18091877 titled 'PEROVSKITE SYNTHESIZABILITY PREDICTION METHOD USING GRAPH CONVOLUTIONAL NEURAL NETWORKS AND POSITIVE UNLABELED LEARNING

'''Simplified Explanation''':
The patent application describes a method for predicting the synthesizability of perovskite using a graph convolutional neural network and positive unlabeled learning.

'''Key Features and Innovation''':
* Utilizes a graph convolutional neural network for predicting perovskite synthesizability.
* Incorporates positive unlabeled learning, a semi-supervised learning technique.
* Based on a labeled model using positive data and positive unlabeled data.

'''Potential Applications''':
This technology can be applied in materials science research, specifically in predicting the synthesizability of perovskite compounds.

'''Problems Solved''':
* Provides a more efficient and accurate method for predicting perovskite synthesizability.
* Helps researchers in identifying potential perovskite materials for various applications.

'''Benefits''':
* Saves time and resources in the materials discovery process.
* Enhances the understanding of perovskite materials and their properties.

'''Commercial Applications''':
Potential commercial applications include materials design, renewable energy technologies, and electronic devices.

'''Questions about Perovskite Synthesizability''':
1. What are the key advantages of using a graph convolutional neural network in predicting perovskite synthesizability?
2. How does positive unlabeled learning improve the accuracy of predicting perovskite synthesizability?

'''Frequently Updated Research''':
Stay updated on the latest advancements in graph convolutional neural networks and positive unlabeled learning techniques for materials science research.


==Original Abstract Submitted==

Provided is a method for predicting perovskite synthesizability using a graph convolutional neural network and positive unlabeled learning, capable of predicting perovskite synthesizability by using a graph convolutional neutral network and positive unlabeled learning which is semi-supervised learning based on a labeled model using positive data and positive unlabeled data.



[[Category:G06N3/08]]
[[Category:CPC_G06N3/08]]