Computational screening of doping schemes for LiTi2(PO4)3 as cathode coating materials

In all-solid-state lithium batteries,the impedance at the cathode/electrolyte interface shows close relationship with the cycle performance.Cathode coatings are helpful to reduce the impedance and increase the stability at the interface effectively.LiTi2(PO4)3(LTP),a fast ion conductor with high ionic conductivity approaching 10^(-3)S·cm^(-1),is adopted as the coating materials in this study.The crystal and electronic structures,as well as the Li^+ion migration properties are evaluated for LTP and its doped derivatives based on density functional theory(DFT)and bond valence(BV)method.Substituting part of Ti sites with element Mn,Fe,or Mg in LTP can improve the electronic conductivity of LTP while does not decrease its high ionic conductivity.In this way,the coating materials with both high ionic conductivities and electronic conductivities can be prepared for all-solid-state lithium batteries to improve the ion and electron transport properties at the interface.

Learn Robust Pedestrian Representation Within Minimal Modality Discrepancy for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification has attracted extensive attention from the community due to its potential great application prospects in video surveillance.There are huge modality discrepancies between visible and infrared images caused by different imaging mechanisms.Existing studies alleviate modality discrepancies by aligning modality distribution or extracting modality-shared features on the original image.However,they ignore a key solution,i.e.,converting visible images to gray images directly,which is efficient and effective to reduce modality discrepancies.In this paper,we transform the cross-modality person re-identification task from visible-infrared images to gray-infrared images,which is named as the minimal modality discrepancy.In addition,we propose a pyramid feature integration network(PFINet)which mines the discriminative refined features of pedestrian images and fuses high-level and semantically strong features to build a robust pedestrian representation.Specifically,PFINet first performs the feature extraction from concrete to abstract and the top-down semantic transfer to obtain multi-scale feature maps.Second,the multi-scale feature maps are inputted to the discriminative-region response module to emphasize the identity-discriminative regions by the spatial attention mechanism.Finally,the pedestrian representation is obtained by the feature integration.Extensive experiments demonstrate the effectiveness of PFINet which achieves the rank-1 accuracy of 81.95%and mAP of 74.49%on the multi-all evaluation mode of the SYSU-MM01 dataset.