Efficient exploration of high-Tc superconductors by a gradient-based composition design

arXiv:2403.13627v1 Announce Type: cross
Abstract: We propose a material design method via gradient-based optimization on compositions, overcoming the limitations of traditional methods: exhaustive database searches and conditional generation models. It optimizes inputs via backpropagation, aligning the model's output closely with the target property and facilitating the discovery of unlisted materials and precise property determination. Our method is also capable of adaptive optimization under new conditions without retraining. Applying to exploring high-Tc superconductors, we identified potential compositions beyond existing databases and …

abstract arxiv backpropagation cond-mat.mtrl-sci cond-mat.supr-con cs.lg database design discovery exploration gradient inputs limitations material materials optimization property superconductors type via