  Extracting meaningful features from complex, high-dimensional datasets across
scientific domains remains challenging. Current methods often struggle with
scalability, limiting their applicability to large datasets, or make
restrictive assumptions about feature-property relationships, hindering their
ability to capture complex interactions. BoUTS's general and scalable feature
selection algorithm surpasses these limitations to identify both universal
features relevant to all datasets and task-specific features predictive for
specific subsets. Evaluated on seven diverse chemical regression datasets,
BoUTS achieves state-of-the-art feature sparsity while maintaining prediction
accuracy comparable to specialized methods. Notably, BoUTS's universal features
enable domain-specific knowledge transfer between datasets, and suggest deep
connections in seemingly-disparate chemical datasets. We expect these results
to have important repercussions in manually-guided inverse problems. Beyond its
current application, BoUTS holds immense potential for elucidating data-poor
systems by leveraging information from similar data-rich systems. BoUTS
represents a significant leap in cross-domain feature selection, potentially
leading to advancements in various scientific fields.
