Data‐augmented machine learning for inverse design of homopolymers with targeted glass transition temperature
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DOI: 10.1002/pi.6788
Abstract: Establishing a reliable workflow of inverse design by data‐driven machine learning (ML) models offers significant potential to accelerate molecular design of polymeric materials. Nevertheless, there exist scarcity issues of training datasets in current data‐driven models… read more here.
Keywords: transition temperature; machine learning; inverse design; targeted glass ... See more keywords