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Extensive efforts to gather materials data have largely overlooked potentialdata redundancy. In this study, we present evidence of a significant degree ofredundancy across multiple large datasets for various material properties, byrevealing that up to 95 % of data can be safely removed from machine learningtraining with little impact on in-distribution prediction performance. Theredundant data is related to over-represented material types and does notmitigate the severe performance degradation on out-of-distribution samples. Inaddition, we show that uncertainty-based active learning algorithms canconstruct much smaller but equally informative datasets. We discuss theeffectiveness of informative data in improving prediction performance androbustness and provide insights into efficient data acquisition and machinelearning training. This work challenges the "bigger is better" mentality andcalls for attention to the information richness of materials data rather than anarrow emphasis on data volume.

On the redundancy in large material datasets: efficient and robust learning with less data