Reporting their findings in the open-access journal npj Computational Materials, the researchers show that their ML method, involving "transfer learning," enables the discovery of materials with desired properties even from an exceeding small data set.
The study drew on a data set of polymeric properties from PoLyInfo, the largest database of polymers in the world housed at NIMS. Despite its huge size, PoLyInfo has a limited amount of data on the heat transfer properties of polymers. To predict the heat transfer properties from the given limited data, ML models on proxy properties were pre-trained where sufficient data were available on the related tasks; these pre-trained models captured common features relevant to the target task. Re-purposing such machine-acquired features on the target task yielded outstanding prediction performance even with the exceedingly small datasets, as if highly experienced human experts can make rational inferences even for considerably less experienced tasks. The team combined this model with a specially designed ML algorithm for computational molecular design, which is called the iQSPR algorithm previously developed by Yoshida and his colleagues. Applying this technique enabled the identification of thousands of promising "virtual" polymers.
Pool - Candidates - Polymers - Ease - Synthesis
From this large pool of candidates, three polymers were selected based on their ease of synthesis and processing.
Tests confirmed that the new polymers have a...
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