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Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery
Significant acceleration of the future discovery of novel functional materials requires a fundamental shift from the current materials discovery practice, which is heavily dependent on trial-and-error campaigns and high-throughput screening, to one that builds on knowledge-driven advanced informatic...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682757/ https://www.ncbi.nlm.nih.gov/pubmed/38035192 http://dx.doi.org/10.1016/j.patter.2023.100863 |