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Interpretable Deep Learning Model for Analyzing the Relationship between the Electronic Structure and Chemisorption Property

Hong, Doosun and Oh, Jaehoon and Bang, Kihoon and Kwon, Soonho and Yun, Se-Young and Lee, Hyuck Mo (2022) Interpretable Deep Learning Model for Analyzing the Relationship between the Electronic Structure and Chemisorption Property. Journal of Physical Chemistry Letters, 13 (37). pp. 8628-8634. ISSN 1948-7185. doi:10.1021/acs.jpclett.2c02293.

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The use of machine learning (ML) is exploding in materials science as a result of its high predictive performance of material properties. Tremendous trainable parameters are required to build an outperforming predictive model, which makes it impossible to retrace how the model predicts well. However, it is necessary to develop a ML model that can extract human-understandable knowledge while maintaining performance for a universal application to materials science. In this study, we developed a deep learning model that can interpret the correlation between surface electronic density of states (DOSs) of materials and their chemisorption property using the attention mechanism that provides which part of DOS is important to predict adsorption energies. The developed model constructs the well-known d-band center theory without any prior knowledge. This work shows that human-interpretable knowledge can be extracted from complex ML models.

Item Type:Article
Related URLs:
URLURL TypeDescription
Oh, Jaehoon0000-0002-4298-1762
Bang, Kihoon0000-0002-5067-034X
Kwon, Soonho0000-0002-9225-3018
Lee, Hyuck Mo0000-0003-4556-6692
Additional Information:This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2017R1E1A1A03071049) and the KAIST-funded Global Singularity Research Program for 2020 and 2021 under Award 1711100689.
Funding AgencyGrant Number
National Research Foundation of KoreaNRF-2017R1E1A1A03071049
Korea Advanced Institute of Science and Technology1711100689
Issue or Number:37
Record Number:CaltechAUTHORS:20221013-48885100.17
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117406
Deposited By: Research Services Depository
Deposited On:18 Oct 2022 22:34
Last Modified:18 Oct 2022 22:34

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