Capturing chemical intuition in synthesis of metal-organic frameworks

Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?

(2018) Boris Maryasin et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Predicting reaction performance in C–N cross-coupling using machine learning

(2018) Derek T. Ahneman et al.   SCIENCE

Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS

(2018) Shishi Lin et al.   SCIENCE

Optimizing Chemical Reactions with Deep Reinforcement Learning

(2017) Zhenpeng Zhou et al.   ACS Central Science

Discovery and Optimization of Materials Using Evolutionary Approaches

(2016) Tu C. Le et al.   CHEMICAL REVIEWS

Machine-learning-assisted materials discovery using failed experiments

(2016) Paul Raccuglia et al.   NATURE

Neural Networks for the Prediction of Organic Chemistry Reactions

(2016) Jennifer N. Wei et al.   ACS Central Science

High-rate synthesis of Cu–BTC metal–organic frameworks

(2013) Ki-Joong Kim et al.   CHEMICAL COMMUNICATIONS

Methane Storage in Metal–Organic Frameworks: Current Records, Surprise Findings, and Challenges

(2013) Yang Peng et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Thermochemistry of Paddle Wheel MOFs: Cu-HKUST-1 and Zn-HKUST-1

(2013) Manas K. Bhunia et al.   LANGMUIR

The Chemistry and Applications of Metal-Organic Frameworks

(2013) H. Furukawa et al.   SCIENCE

Synthesis of Metal-Organic Frameworks (MOFs): Routes to Various MOF Topologies, Morphologies, and Composites

(2011) Norbert Stock et al.   CHEMICAL REVIEWS

High-throughput screening of synthesis parameters in the formation of the metal-organic frameworks MOF-5 and HKUST-1

(2008) Enrica Biemmi et al.   MICROPOROUS AND MESOPOROUS MATERIALS