Classification-based detection and quantification of cross-domain data bias in materials discovery
This addresses bias issues in materials discovery, particularly for energy materials, but is incremental as it applies a classification strategy to a known bottleneck.
The paper tackles the problem of cross-domain data bias in AI-driven materials discovery, presenting a classification-based method that detects, quantifies, and circumvents bias, validated on superconducting and thermoelectric materials.
It stands to reason that the amount and the quality of data is of key importance for setting up accurate AI-driven models. Among others, a fundamental aspect to consider is the bias introduced during sample selection in database generation. This is particularly relevant when a model is trained on a specialized dataset to predict a property of interest, and then applied to forecast the same property over samples having a completely different genesis. Indeed, the resulting biased model will likely produce unreliable predictions for many of those out-of-the-box samples. Neglecting such an aspect may hinder the AI-based discovery process, even when high quality, sufficiently large and highly reputable data sources are available. In this regard, with superconducting and thermoelectric materials as two prototypical case studies in the field of energy material discovery, we present and validate a new method (based on a classification strategy) capable of detecting, quantifying and circumventing the presence of cross-domain data bias.