Cargando…
Inverse design of porous materials using artificial neural networks
Generating optimal nanomaterials using artificial neural networks can potentially lead to a notable revolution in future materials design. Although progress has been made in creating small and simple molecules, complex materials such as crystalline porous materials have yet to be generated using any...
Autores principales: | Kim, Baekjun, Lee, Sangwon, Kim, Jihan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6941911/ https://www.ncbi.nlm.nih.gov/pubmed/31922005 http://dx.doi.org/10.1126/sciadv.aax9324 |
Ejemplares similares
-
Nanophotonic particle simulation and inverse design using artificial neural networks
por: Peurifoy, John, et al.
Publicado: (2018) -
Inverse design of core-shell particles with discrete material classes using neural networks
por: Kuhn, Lina, et al.
Publicado: (2022) -
Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks
por: Li, Dongshuang, et al.
Publicado: (2023) -
Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning
por: Chen, Chun‐Teh, et al.
Publicado: (2020) -
Inverse Estimation of Moisture Diffusion Model for Concrete Using Artificial Neural Network
por: Lee, Jae Min, et al.
Publicado: (2022)