  Training data are usually limited or heterogeneous in many chemical and
biological applications. Existing machine learning models for chemistry and
materials science fail to consider generalizing beyond training domains. In
this article, we develop a novel optimal transport-based algorithm termed MROT
to enhance their generalization capability for molecular regression problems.
MROT learns a continuous label of the data by measuring a new metric of domain
distances and a posterior variance regularization over the transport plan to
bridge the chemical domain gap. Among downstream tasks, we consider basic
chemical regression tasks in unsupervised and semi-supervised settings,
including chemical property prediction and materials adsorption selection.
Extensive experiments show that MROT significantly outperforms state-of-the-art
models, showing promising potential in accelerating the discovery of new
substances with desired properties.
