3 papers
cond-mat.mtrl-sci2026
Ab initio-based Deep-Learning Prediction of Carrier Mobility in Strongly Anharmonic Materials
Juan Zhang, Boheng Zhao, Yang Li +4
Predicting charge transport in strongly anharmonic materials, particularly ultralow thermal conductors, remains a major challenge for first-principles methods. In such systems, per…
physics.comp-ph2026
AI-accelerated metallized -bonding screening for superconductor discovery
Zechen Tang, Wen-Han Dong, Baochun Wu +11
The computational discovery of phonon-mediated superconductors is hindered by the prohibitive cost of density functional perturbation theory (DFPT). Here, guided by the metallized…
cond-mat.mtrl-sci2026
DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations
Yang Li, Yanzhen Wang, Boheng Zhao +15
In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and…