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Related Experiment Video

Updated: Jun 17, 2025

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Published on: June 7, 2018

Boosting-Crystal Graph Convolutional Neural Network for Predicting Highly Imbalanced Data: A Case Study for

Eun Ho Kim¹, Jun Hyeong Gu¹, June Ho Lee¹

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

ACS Applied Materials & Interfaces

|August 9, 2024

Summary

This summary is machine-generated.

Machine learning for imbalanced materials science data is challenging. Boosting-CGCNN, a deep learning framework, effectively predicts minority-class metal-insulator transition materials, outperforming other methods.

Keywords:

deep learning gradient boosting graph neural network (GNN)imbalanced data inverse design metal−insulator transition (MIT)

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Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

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Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

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Published on: October 12, 2019

Area of Science:

Materials Science
Machine Learning
Deep Learning

Background:

Imbalanced datasets in materials science pose challenges for machine learning.
Existing methods like oversampling can cause information loss or overfitting.

Purpose of the Study:

To develop a deep learning framework for predicting minority-class materials, focusing on metal-insulator transition (MIT) materials.
To address extreme class imbalances in materials data.

Main Methods:

Introduced boosting-CGCNN, combining crystal graph convolutional neural network (CGCNN) with gradient boosting.
Sequentially built a deeper neural network to handle class imbalances.

Main Results:

The boosting-CGCNN model effectively handled extreme class imbalances in MIT material data.
Demonstrated superior performance compared to existing approaches through comparative evaluations.

Conclusions:

Boosting-CGCNN offers a promising solution for handling imbalanced datasets in materials science.
The framework is particularly effective for predicting minority-class materials like MIT materials.