High-performance Li-ion battery significantly impacts modern society, and materials with high conductivity play critical roles in battery development. Machine learning (ML) technologies have rapidly changed the field in recent years. However, it is still challenging to predict the high conductors directly due to the lack of validated conductor samples. This paper presents a succinct but effective metric-learning framework for high conductor screening. The material structures are mapped to an optimized feature space using a Siamese network, and an instance-based method is used to classify the input sample. The experiments demonstrate that the proposed method could effectively extract knowledge from imbalanced data and has good performance and generalization ability.
Keywords: Conductor screening; Contrastive learning; Graph neural network; Material science; Metric learning.