Transferable diversity – a data-driven representation of chemical space

While transferability in general chemistry machine learning should benefit from diverse training data, a rigorous understanding of transferability together with its interplay with chemical representation remains an open problem. In this talk, I will introduce a transferability framework and apply it to a controllable data-driven model for developing density functional approximations (DFAs) [1]. This framework reveals that human intuition introduces chemical biases that can hamper the transferability of data-driven DFAs, and it allowed us to identify strategies for their elimination. I will also show that the uncritical use of large training sets can actually hinder the transferability of DFAs, in contradiction to typical "more is more" expectations. Finally, I will demonstrate how our transferability framework yields transferable diversity, a cornerstone principle for data curation for developing general-purpose machine learning models in chemistry.