3 papers
physics.app-ph2026
Machine learning-enabled inverse design of bimaterial thermoelastic lattice metamaterials
Xiang-Long Peng, Bai-Xiang Xu
The thermoelastic metamaterial based on a bimaterial hybrid-honeycomb structure, exhibiting simultaneously negative Poisson's ratios and negative thermal expansion coefficients is…
cond-mat.mtrl-sci2025
Deep learning-enabled large-scale analysis of particle geometry-lithiation correlations in battery cathode materials
Binbin Lin, Luis J. Carrillo, Xiang-Long Peng +4
A deep learning model is employed to address the challenging problem of V2O5 nanoparticle segmentation and the correlation between the chemical composition and the geometrical feat…
cond-mat.mtrl-sci2025
Deciphering the interplay between wetting and chemo-mechanical fracture in lithium-ion battery cathode materials
Wan-Xin Chen, Luis J. Carrillo, Arnab Maji +4
Crack growth in lithium-ion battery electrodes is typically detrimental and undesirable. However, recent experiments suggest that stabilized fracture of cathode active materials in…