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Towards understanding structure-property relations in materials with interpretable deep learning

Engineering and Technology

Towards understanding structure-property relations in materials with interpretable deep learning

T. Vu, M. Ha, et al.

This innovative research, conducted by a team of experts, unveils a deep learning architecture that leverages attention mechanisms to predict material properties and decode complex structure-property relationships. The findings reveal how local atomic structures play a pivotal role in determining critical properties, setting a new direction for accelerated material design.

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Playback language: English
Abstract
This paper proposes an interpretable deep learning architecture incorporating the attention mechanism to predict material properties and understand structure-property relationships. The architecture's predictive capabilities are validated using various datasets, showing comparable performance to state-of-the-art models. Furthermore, analysis indicates the importance of atomic local structures in interpreting structure-property relationships for properties like molecular orbital energies and formation energies. The architecture's ability to identify crucial structural features accelerates material design.
Publisher
npj Computational Materials
Published On
Jan 31, 2023
Authors
Tien-Sinh Vu, Minh-Quyet Ha, Duong-Nguyen Nguyen, Viet-Cuong Nguyen, Yukihiro Abe, Truyen Tran, Huan Tran, Hiori Kino, Takashi Miyake, Koji Tsuda, Hieu-Chi Dam
Tags
deep learning
attention mechanism
material properties
structure-property relationships
atomic structures
predictive modeling

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