摘要
Machine learning has been widely used to guide the search for new materials by learning the patterns underlying available data.However,the pure prediction-oriented search is often biased to interpolation due to the limited data in a large unexplored space.Here we present a sampling framework towards extrapolation,that integrates unsupervised clustering,interpretable analysis,and similarity evaluation to sample target candidates with improved properties from a vast search space.
基金
supported by the National Key Research and Development Program of China(2021YFB3702604)
the National Natural Science Foundation of China(52002326)
the Research Fund of the State Key Laboratory of Solidification Processing(NPU),China(grant no.2023-TS-12).
