Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis

Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data‐driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between...

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Detalles Bibliográficos
Autores principales: Zhang, Shuo‐Qing, Xu, Li‐Cheng, Li, Shu‐Wen, Oliveira, João C. A., Li, Xin, Ackermann, Lutz, Hong, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Reviews
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099903/
https://www.ncbi.nlm.nih.gov/pubmed/36206170
http://dx.doi.org/10.1002/chem.202202834

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099903/
https://www.ncbi.nlm.nih.gov/pubmed/36206170
http://dx.doi.org/10.1002/chem.202202834

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