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• W2904181725 abstract "Over the past decade, there has been a resurgence in the importance of data-driven techniques in material science and engineering. The utilization of state-of-the art algorithms, coupled with the increased availability of experimental and computational data, has led to the development of surrogate models offering the promise of rapid and accurate predictions of materials properties based solely on their structure or composition. Such machine learning models are trained on available past data and are thus susceptible to the intrinsic uncertainties/errors associate with these past measurements. The glass transition temperature (Tlsubggl/subg) of polymers, a property of paramount interest in polymer science, is one strong example of a material property that can show widespread variation in the final reported value as a result of a variety of intrinsic and extrinsic factors that occur during the experimental measurement process. In the current work, we curate a large database of Tlsubggl/subg measurements from a variety of data sources and proceed to investigate the statistical nature of the inherent uncertainties in the database. Through the partitioning of the dataset using statistically relevant measures, we investigate the effect of variations in the dataset on the performance of the final machine learning model. We demonstrate that measures of central tendency (such as mean and median) are valid approximations when dealing with multiple reported values of Tlsubggl/subg for the same polymeric material. Moreover, the Bayesian model noise/uncertainty that emerges from our machine-learning pipeline is able to represent quantitatively the underlying noise/uncertainties in the experimental measurement of Tlsubggl/subg." @default.
• W2904181725 created "2018-12-22" @default.
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• W2904181725 date "2019-01-17" @default.
• W2904181725 modified "2023-09-29" @default.
• W2904181725 title "Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures" @default.
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• W2904181725 doi "https://doi.org/10.1088/1361-651x/aaf8ca" @default.
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