Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
Knowing compositional motifs of nanoparticle catalysts in operando conditions is crucial towards understanding their catalytic behavior. Here, the authors develop a physics-driven machine learning approach to predict adsorption sites for a CO molecule over platinum nanoparticles in a multitude of co...
Guardado en:
Autores principales: | Joshua L. Lansford, Dionisios G. Vlachos |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/6fd23d16dcac44afbf726b5db3d22040 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Scaling relationships and theory for vibrational frequencies of adsorbates on transition metal surfaces
por: Joshua L. Lansford, et al.
Publicado: (2017) -
Unravelling the structural complexity of glycolipids with cryogenic infrared spectroscopy
por: Carla Kirschbaum, et al.
Publicado: (2021) -
Infrared spectroscopy of the surface of thermally-modified teak juvenile wood
por: Lopes,Juliana de Oliveira, et al.
Publicado: (2018) -
Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies
por: Himaghna Bhattacharjee, et al.
Publicado: (2021) -
Physical properties of Guazuma crinita by conventional methods and near infrared spectroscopy
por: Chavesta,Manuel, et al.
Publicado: (2019)