2023/11/16 by Giovanni Trezza, Trezza, Giovanni, Eliodoro Chiavazzo +1 · 2 citations
Computer Science · Materials Science · #Expert finding and Q&A systems #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Other Condensed Matter (cond-mat.other)
paper · pdf · doi:10.48550/arxiv.2311.09891
openalex publication_date 2023/11/16 · openalex created_date 2023/11/18 · openalex updated_date 2026/07/28
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.