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

npj Computational Materials 2023年第1期9卷 157-168页

作者： 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 Japan Advanced Institute of Science and Technology NomiIshikawaJapan HPC SYSTEMS Inc. MinatoTokyoJapan Applied Artificial Intelligence Institute Deakin UniversityGeelongVICAustralia School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGAUSA Research and Services Division of Materials Data and Integrated System National Institute for Materials ScienceTsukubaIbarakiJapan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and TechnologyTsukubaIbarakiJapan Department of Computational Biology and Medical Sciences Graduate School of Frontier SciencesUniversity of TokyoKashiwa-shiChiba-kenJapan International Center for Synchrotron Radiation Innovation Smart(SRIS) Tohoku UniversityAoba-kuSendaiJapan

Deep 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 *** 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 *** proposed architecture is evaluated using two well-known datasets(the QM9 and the Materials Project datasets),and three in-house-developed computational materials ***–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 ***,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 encompass molecular orbital energies and the formation energies of *** proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

关键词： properties property structure

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Enhancement of water absorption capacity and compressibility of hydrogel sponges prepared from gelatin/chitosan matrix with different polyols

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Progress in Natural Science:Materials International 2022年第1期32卷 54-62页

作者： Thi sinh Vo Tran Thi Bich Chau Vo Trung tien Tran Nhan Duy Pham School of Mechanical Engineering Sungkyunkwan University Department of Industrial Management Can Tho University Department of Chemistry Memorial University of Newfoundland

The hydrogel sponges have prepared successfully from gelatin/chitosan(GEL/CTS) matrix with different polyols basing on scanning electron microscopy(SEM) and Fourier-transform infrared spectroscopy(FTIR) analysis. The results indicate that the compressibility and thermal properties(thermal gravimetric analysis, TGA) were improved significantly with adding the different polyols [poly(vinyl alcohol)-PVA, and poly(ethylene glycol)-poly(propylene glycol)-poly(ethylene glycol) – PEPEG] into GEL/CTS matrix. Specially, the as-prepared hydrogel sponges with superabsorbent behavior were also investigated with the attained water absorption capacity to 605 g gand 585 g gfor GCV＿1/1/0.3 and GCE＿1/1/0.3 sponges, respectively, as well as the excellent water absorption capacity in a wide range of p H and salt concentration. Hence, these studies demonstrated that the hydrogel sponges are concerned as potential and promising candidates to apply especially in the fields of biomedicine and hygiene.

关键词： Hydrogel sponges Absorbent Chitosan Polyols Gelatin Water absorption capacity

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