Predictive chemistry often struggles with scarce data. Surrogate models can help, but should we use their predicted QM descriptors or hidden embeddings? Chen & Stuyver show that hidden spaces usually win—faster, more robust, and data-efficient. pubs.rsc.org/en/content/a...
Harnessing surrogate models for data-efficient predictive chemistry: descriptors vs. learned hidden representations
Predictive chemistry often faces data scarcity, limiting the performance of machine learning (ML) models. This is particularly the case for specialized tasks such as reaction rate or selectivity predi...
pubs.rsc.org
