Cargando…
Deep neural network learning of complex binary sorption equilibria from molecular simulation data
We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling. Canonical (N(1)N(2)VT) Gibbs ensemble Monte Carlo simulations were performed to model a single-stage equilibrium desorptive drying p...
Autores principales: | Sun, Yangzesheng, DeJaco, Robert F., Siepmann, J. Ilja |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Royal Society of Chemistry
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6482883/ https://www.ncbi.nlm.nih.gov/pubmed/31057764 http://dx.doi.org/10.1039/c8sc05340e |
Ejemplares similares
-
Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning
por: Sun, Yangzesheng, et al.
Publicado: (2021) -
First-Principles Monte Carlo Simulations of Reaction
Equilibria in Compressed Vapors
por: Fetisov, Evgenii O., et al.
Publicado: (2016) -
Understanding liquid–liquid equilibria in binary mixtures of hydrocarbons with a thermally robust perarylphosphonium-based ionic liquid
por: Bandlamudi, Santosh R. P., et al.
Publicado: (2021) -
Phase equilibria in the neodymium–cadmium binary system
por: Skołyszewska-Kühberger, Barbara, et al.
Publicado: (2014) -
Efficient Sorption of Arsenic on Nanostructured Fe-Cu Binary Oxides: Influence of Structure and Crystallinity
por: Zhang, Gaosheng, et al.
Publicado: (2022)