Cargando…
BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules
A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental t...
Autores principales: | Wen, Mingjian, Blau, Samuel M., Spotte-Smith, Evan Walter Clark, Dwaraknath, Shyam, Persson, Kristin A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179073/ https://www.ncbi.nlm.nih.gov/pubmed/34163950 http://dx.doi.org/10.1039/d0sc05251e |
Ejemplares similares
-
A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation
por: Blau, Samuel M., et al.
Publicado: (2021) -
Quantum chemical calculations of lithium-ion battery electrolyte and interphase species
por: Spotte-Smith, Evan Walter Clark, et al.
Publicado: (2021) -
SL-HarDNet: Skin lesion segmentation with HarDNet
por: Bai, Ruifeng, et al.
Publicado: (2023) -
Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining
por: Wen, Mingjian, et al.
Publicado: (2022) -
Bon-Bons
Publicado: (1906)