Cargando…
Ab Initio Machine Learning in Chemical Compound Space
[Image: see text] Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling of this space, for example, in search of novel molecules or materials...
Autores principales: | Huang, Bing, von Lilienfeld, O. Anatole |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391942/ https://www.ncbi.nlm.nih.gov/pubmed/34387476 http://dx.doi.org/10.1021/acs.chemrev.0c01303 |
Ejemplares similares
-
Density Functional Geometries and Zero-Point Energies
in Ab Initio Thermochemical Treatments of Compounds with First-Row
Atoms (H, C, N, O, F)
por: Bakowies, Dirk, et al.
Publicado: (2021) -
Retrospective on a decade of machine learning for chemical discovery
por: von Lilienfeld, O. Anatole, et al.
Publicado: (2020) -
Machine Learning of Parameters for Accurate Semiempirical
Quantum Chemical Calculations
por: Dral, Pavlo O., et al.
Publicado: (2015) -
Machine Learning Predictions of Molecular Properties:
Accurate Many-Body Potentials and Nonlocality in Chemical Space
por: Hansen, Katja, et al.
Publicado: (2015) -
Data enhanced Hammett-equation: reaction barriers in chemical space
por: Bragato, Marco, et al.
Publicado: (2020)