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While machine learning has emerged in recent years as a useful tool for rapidprediction of materials properties, generating sufficient data to reliablytrain models without overfitting is still impractical for many applications.Towards overcoming this limitation, we present a general framework forleveraging complementary information across different models and datasets foraccurate prediction of data scarce materials properties. Our approach, based ona machine learning paradigm called mixture of experts, outperforms pairwisetransfer learning on 16 of 19 materials property regression tasks, performingcomparably on the remaining three. Unlike pairwise transfer learning, ourframework automatically learns to combine information from multiple sourcetasks in a single training run, alleviating the need for brute-forceexperiments to determine which source task to transfer from. The approach alsoprovides an interpretable, model-agnostic, and scalable mechanism to transferinformation from an arbitrary number of models and datasets to any downstreamproperty prediction task. We anticipate the performance of our framework willfurther improve as better model architectures, new pre-training tasks, andlarger materials datasets are developed by the community.

Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework