Cargando…
Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis
[Image: see text] The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experim...
| Autores principales: | , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Chemical Society
2023
|
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10565808/ https://www.ncbi.nlm.nih.gov/pubmed/37767941 http://dx.doi.org/10.1021/acs.inorgchem.3c02697 |