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A simple denoising approach to exploit multi-fidelity data for machine learning materials properties
Machine-learning models have recently encountered enormous success for predicting the properties of materials. These are often trained based on data that present various levels of accuracy, with typically much less high- than low-fidelity data. In order to extract as much information as possible from all available data, we here introduce an approach which aims to improve the quality of the data through denoising. We investigate the possibilities that it offers in the case of the prediction ofdoi:10.21203/rs.3.rs-1579892/v1 fatcat:f2n4tpc4jnbxpotdpbvbls6zk4