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

journal contribution
posted on 2023-12-20, 01:54 authored by TS Vu, MQ Ha, DN Nguyen, VC Nguyen, Y Abe, Truyen TranTruyen Tran, H Tran, H Kino, T Miyake, K Tsuda, HC Dam
AbstractDeep learning (DL) models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material properties. To address these limitations, we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property relationships. The proposed architecture is evaluated using two well-known datasets (the QM9 and the Materials Project datasets), and three in-house-developed computational materials datasets. Train–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities, which are comparable to those of current state-of-the-art models. Furthermore, comparative validations, based on first-principles calculations, indicate that the degree of attention of the atoms’ local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical properties. These properties encompass molecular orbital energies and the formation energies of crystals. The proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

History

Journal

npj Computational Materials

Volume

9

Article number

215

Pagination

1-12

Location

Berlin, Germany

ISSN

2057-3960

eISSN

2057-3960

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

1

Publisher

Nature

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