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EMMC and Kim Jelfs (Imperial College London) present the CASE STUDY

“Machine Learning for Organic Cage Property Prediction”

Interview of Kim Jelfs, Imperial College London
Writers: Alexandra Simperler and Gerhard Goldbeck (Goldbeck Consulting)

The case describes how they use machine learning to predict shape persistence and cavity size in porous organic cages.
Therefore, they created the largest computational database of these molecules comprising 63,472 cages, ….

thumbnail of EMMC_KJELFS_CASESTUDY

        
 

Support

This work has received funding via the EMMC-CSA project 
from the European Union‘s Horizon 2020 research and innovation programme
under Grant Agreement No 723867 

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