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Materials property prediction models are usually evaluated using random
splitting of datasets into training and test datasets, which not only leads to
over-estimated performance due to inherent redundancy, typically existent in
material datasets, but also deviate away from the common practice of materials
scientists: they are usually interested in predicting properties for a known
subset of related out-of-distribution (OOD) materials rather than a universally
distributed samples. Feeding such target material formulas/structures to the
machine learning models should improve the prediction performance while most
current machine learning (ML) models neglect this information. Here we propose
to use domain adaptation (DA) to enhance current ML models for property
prediction and evaluate their performance improvements in a set of five
realistic application scenarios. Our systematic benchmark studies show that
there exist DA models that can significantly improve the OOD test set
prediction performance while standard ML models and most of the other DAs
cannot improve or even deteriorate the performance. Our benchmark datasets and
DA code can be freely accessed at https://github.com/Little-Cheryl/MatDA.
No creative common's license