Framework for discovering porous materials: Structural hybridization and Bayesian optimization of conditional generative adversarial network

Framework for discovering porous materials: Structural hybridization and Bayesian optimization of conditional generative adversarial network

Although deep-learning-based materials discovery has attracted considerable research attention, the application of deep learning has been limited to discovery of materials within single material types such that the discovered materials are similar to existing ones. Thus, we developed an alternative...

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Bibliographic Details
Main Authors: Yosuke Matsuda, Shinichi Ookawara, Tomoki Yasuda, Shiro Yoshikawa, Hideyuki Matsumoto
Format: Article
Language:English
Published: Elsevier 2022-12-01
Series:Digital Chemical Engineering
Subjects:
Porous material
Permeability
Filtration efficiency
Conditional generative adversarial network
Computational fluid dynamics simulation
Multiobjective Bayesian optimization
Online Access:http://www.sciencedirect.com/science/article/pii/S2772508122000485

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http://www.sciencedirect.com/science/article/pii/S2772508122000485

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