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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Improving deep learning model performance under parametric constraints for materials informatics applications.

Vishu Gupta¹, Alec Peltekian¹, Wei-Keng Liao¹

¹Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA.

Scientific Reports

|June 5, 2023

Summary

This summary is machine-generated.

Researchers developed a new deep learning framework, branched residual learning (BRNet), for accelerated materials discovery. This approach improves accuracy and training speed for predicting material properties, outperforming traditional machine learning and deep learning models.

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Area of Science:

Materials Science
Computational Chemistry
Machine Learning

Background:

Machine learning (ML) and deep learning (DL) accelerate materials discovery by analyzing complex datasets.
Fully connected deep neural networks are common for materials property prediction but suffer from performance degradation due to the vanishing gradient problem in deeper architectures.
Existing methods face limitations in performance and efficiency under fixed parametric constraints.

Purpose of the Study:

To propose architectural principles for enhancing the performance of model training and inference in deep learning for materials science.
To introduce a novel deep learning framework, branched residual learning network (BRNet), to address the limitations of traditional deep neural networks.
To improve the accuracy and efficiency of predicting materials properties using vector-based data representations.

Main Methods:

Developed a general deep learning framework, BRNet, utilizing branched residual learning with fully connected layers.
Input data comprised numerical vectors representing composition-based attributes of materials.
Trained and compared BRNet models against traditional ML and existing DL architectures for materials property prediction across various data sizes.

Main Results:

BRNet models demonstrated significantly higher accuracy compared to traditional ML and existing DL models for all tested data sizes.
The proposed branched learning approach requires fewer parameters than existing neural networks.
BRNet achieved faster model training due to improved convergence during the training phase.

Conclusions:

Branched residual learning (BRNet) offers a more accurate and efficient approach to materials property prediction.
The framework effectively overcomes the vanishing gradient problem, enabling better performance under fixed parametric constraints.
BRNet accelerates the materials discovery process by providing accurate predictions with reduced computational resources.