Articlenpj computational materials2023
An interpretable deep learning approach for designing nanoporous silicon nitride membranes with tunable mechanical properties.
Article in npj computational materials, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Rust-accelerated powder X-ray diffraction simulation for high-throughput and machine-learning-driven materials science.Journal of applied crystallography · 2026Article
- Spectroscopy-informed XANES-PXRD framework for multi-property prediction and structure inference.Chemical science · 2026Article
- Machine learning approaches for crystallographic classification from synthetic 2D X-ray diffraction data.Journal of applied crystallography · 2026Article
- Machine Learning for Thermal Transport Prediction in Nanoporous Materials: Progress, Challenges, and Opportunities.Nanomaterials (Basel, Switzerland) · 2025Review
- Smart and Sustainable Regeneration of Fouled Desalination Membranes Using Artificial Intelligence.Global challenges (Hoboken, NJ) · 2025Review
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Authors and funding
2 authors.
Funding
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Abstract
The high permeability and strong selectivity of nanoporous silicon nitride (NPN) membranes make them attractive in a broad range of applications. Despite their growing use, the strength of NPN membranes needs to be improved for further extending their biomedical applications. In this work, we implement a deep learning framework to design NPN membranes with improved or prescribed strength values. We examine the predictions of our framework using physics-based simulations. Our results confirm that the proposed framework is not only able to predict the strength of NPN membranes with a wide range of microstructures, but also can design NPN membranes with prescribed or improved strength. Our simulations further demonstrate that the microstructural heterogeneity that our framework suggests for the optimized design, lowers the stress concentration around the pores and leads to the strength improvement of NPN membranes as compared to conventional membranes with homogenous microstructures.
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Registered trials
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