Capturing chemical intuition in synthesis of metal-organic frameworks
Synthetic chemists develop a "chemical intuition" over years of experience in the lab. Here the authors combine machine learning of (partially) failed experiments with robotic synthesis to capture this intuition used in searching for the optimal synthesis conditions of metal-organic framew...
Enregistré dans:
Auteurs principaux: | Seyed Mohamad Moosavi, Arunraj Chidambaram, Leopold Talirz, Maciej Haranczyk, Kyriakos C. Stylianou, Berend Smit |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2019
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/52373b3681854fd292ba1296fab6d35b |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Understanding the diversity of the metal-organic framework ecosystem
par: Seyed Mohamad Moosavi, et autres
Publié: (2020) -
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
par: Aditi S. Krishnapriyan, et autres
Publié: (2021) -
Metal–organic framework with optimally selective xenon adsorption and separation
par: Debasis Banerjee, et autres
Publié: (2016) -
Nucleobase pairing and photodimerization in a biologically derived metal-organic framework nanoreactor
par: Samantha L. Anderson, et autres
Publié: (2019) -
Minimalism and Speakers' Intuitions
par: MATÍAS GARIAZZO
Publié: (2011)