Cargando…
Data-Driven Strategies for Accelerated Materials Design
[Image: see text] The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional...
Autores principales: | Pollice, Robert, dos Passos Gomes, Gabriel, Aldeghi, Matteo, Hickman, Riley J., Krenn, Mario, Lavigne, Cyrille, Lindner-D’Addario, Michael, Nigam, AkshatKumar, Ser, Cher Tian, Yao, Zhenpeng, Aspuru-Guzik, Alán |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2021
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7893702/ https://www.ncbi.nlm.nih.gov/pubmed/33528245 http://dx.doi.org/10.1021/acs.accounts.0c00785 |
Ejemplares similares
-
Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES
por: Nigam, AkshatKumar, et al.
Publicado: (2021) -
On scientific understanding with artificial intelligence
por: Krenn, Mario, et al.
Publicado: (2022) -
Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design
por: Nigam, AkshatKumar, et al.
Publicado: (2022) -
Recent advances in the self-referencing embedded strings (SELFIES) library
por: Lo, Alston, et al.
Publicado: (2023) -
Guided discovery of chemical reaction pathways with imposed activation
por: Lavigne, Cyrille, et al.
Publicado: (2022)