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...

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Detalles Bibliográficos
Autores principales: Qian, Xiaoning, Yoon, Byung-Jun, Arróyave, Raymundo, Qian, Xiaofeng, Dougherty, Edward R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Review
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

Internet

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

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