Optimal Surrogate Models for Predicting the Elastic Moduli of Metal–Organic Frameworks via Multiscale Features

Lee, Jaejun; Lee, Inhyo; Park, Jaejung; Kim, Heekyu; Kim, Minseon; Min, Kyoungmin; Lee, Seungchul

doi:10.1021/acs.chemmater.3c01885

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2024

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Cited by 6 publications

(1 citation statement)

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“…In this regard, atom-centered PH , is able to distinguish such a change in the topological configurations of MOFs (see S9 and Figures S10–S12 for an extended discussion). Thus, by combining multiscale descriptors, we aim to provide more holistic understanding in the relationships between various features and thermal expansion properties.…”

Section: Resultsmentioning

confidence: 99%

Classifying and Predicting the Thermal Expansion Properties of Metal–Organic Frameworks: A Data-Driven Approach

Yue,

Mohamed,

Jiang

2024

J. Chem. Inf. Model.

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Section: Resultsmentioning

confidence: 99%

Classifying and Predicting the Thermal Expansion Properties of Metal–Organic Frameworks: A Data-Driven Approach

Yue,

Mohamed,

Jiang

2024

J. Chem. Inf. Model.

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Recent advances on metal-organic frameworks (MOFs) and their applications in energy conversion devices: Comprehensive review

Abdelkareem,

Abbas,

Sayed

et al. 2024

Energy

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Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design

Lee,

Park,

Min

2024

J. Chem. Inf. Model.

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In this study, we introduced Matini-Net, which is a versatile framework for feature engineering and automated architecture design for materials informatics research using deep neural networks. Matini-Net provides the flexibility to design feature-based, graph-based, and combinations of these models, accommodating both single-and multimodal model architectures. For validation, we performed a performance evaluation on the MatBench benchmarking dataset of five properties, targeting five types of regression architectures that can be designed using Matini-Net. When applied to each of the five material property datasets, the best model performance for the various architectures exhibited R 2 > 0.84. This highlights the usefulness and flexibility of Matini-Net for accelerating materials discovery. Specifically, this framework was developed for researchers with limited experience in deep learning to easily apply it to research through automated feature engineering, hyperparameter tuning, and network construction. Moreover, Matini-Net improves the model interpretability by performing an importance analysis of the selected features. We believe that by employing Matini-Net, machine and deep learning can be applied more easily and effectively in various types of materials research.

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Optimal Surrogate Models for Predicting the Elastic Moduli of Metal–Organic Frameworks via Multiscale Features

Cited by 6 publications

References 74 publications

Classifying and Predicting the Thermal Expansion Properties of Metal–Organic Frameworks: A Data-Driven Approach

Classifying and Predicting the Thermal Expansion Properties of Metal–Organic Frameworks: A Data-Driven Approach

Recent advances on metal-organic frameworks (MOFs) and their applications in energy conversion devices: Comprehensive review

Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design

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