Boosting–Crystal Graph Convolutional Neural Network for Predicting Highly Imbalanced Data: A Case Study for Metal–Insulator Transition Materials-Reference-Cited by-同舟云学术

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Boosting–Crystal Graph Convolutional Neural Network for Predicting Highly Imbalanced Data: A Case Study for Metal–Insulator Transition Materials

Published:2024-08-09 Issue:33 Volume:16 Page:43734-43741
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Kim Eun Ho¹,Gu Jun Hyeong¹^ORCID,Lee June Ho¹,Kim Seong Hun¹,Kim Jaeseon¹^ORCID,Shin Hyo Gyeong¹,Kim Shin Hyun¹,Lee Donghwa¹²^ORCID

Affiliation:

1. Department of Materials Science and Engineering (MSE), and Division of Advanced Materials Science (AMS), Pohang University of Science and Technology (POSTECH), Pohang 37673, South Korea

2. Institute for Convergence Research and Education in Advanced Technology (I_CREATE), Yonsei University, Incheon 21983, South Korea

Funder

National Research Foundation of Korea

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.4c07851

Reference45 articles.

1. Scientific discovery in the age of artificial intelligence

2. The central role of density functional theory in the AI age

3. Accelerating the prediction of stable materials with machine learning

4. Theory-guided experimental design in battery materials research

5. Learning the crystal structure genome for property classification

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