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It stands to reason that the amount and the quality of data is of keyimportance for setting up accurate AI-driven models. Among others, afundamental aspect to consider is the bias introduced during sample selectionin database generation. This is particularly relevant when a model is trainedon a specialized dataset to predict a property of interest, and then applied toforecast the same property over samples having a completely different genesis.Indeed, the resulting biased model will likely produce unreliable predictionsfor many of those out-of-the-box samples. Neglecting such an aspect may hinderthe AI-based discovery process, even when high quality, sufficiently large andhighly reputable data sources are available. In this regard, withsuperconducting and thermoelectric materials as two prototypical case studiesin the field of energy material discovery, we present and validate a new method(based on a classification strategy) capable of detecting, quantifying andcircumventing the presence of cross-domain data bias.

Classification-based detection and quantification of cross-domain data bias in materials discovery