3 papers
cond-mat.mtrl-sci2026
Theory-Guided, Machine-Learning-Accelerated Discovery of a 3D Carbon Nested Nodal-Surface Semimetal
Shuaihua Zhang, Silei Guo, Jingxiang Liu +3
Extending the Dirac physics of two-dimensional (2D) graphene into three dimensions (3D) carbon allotropes with higher-dimensional band degeneracies remains a central challenge in t…
cond-mat.supr-con2024
Superband: an Electronic-band and Fermi surface structure database of superconductors
Tengdong Zhang, Chenyu Suo, Yanling Wu +4
In comparison to simpler data such as chemical formulas and lattice structures, electronic band structure data provide a more fundamental and intuitive insight into superconducting…
cond-mat.supr-con2024
A deep learning approach to search for superconductors from electronic bands
Jun Li, Wenqi Fang, Shangjian Jin +5
Energy band theory is a foundational framework in condensed matter physics. In this work, we employ a deep learning method, BNAS, to find a direct correlation between electronic ba…