24 citations · 25 across the 7 of their papers we have counts for
8 papers
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…
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…
Cross-Layer Design of Vector-Symbolic Computing: Bridging Cognition and Brain-Inspired Hardware Acceleration
Shuting Du, Mohamed Ibrahim, Zishen Wan +7
Vector Symbolic Architectures (VSAs) have been widely deployed in various cognitive applications due to their simple and efficient operations. The widespread adoption of VSAs has,…
Fractionalized Kohn-Sham Scheme for Strongly Correlated Electrons
Bo Zhao, Jingyu Zhao, Zheng Zhu +2
We propose to expand the territory of density functional theory to strongly correlated electrons by reformulating the Kohn-Sham scheme in the representation of fractionalized parti…
Deep learning density functional theory Hamiltonian in real space
Zilong Yuan, Zechen Tang, Honggeng Tao +11
Deep learning electronic structures from ab initio calculations holds great potential to revolutionize computational materials studies. While existing methods proved success in dee…
Improving density matrix electronic structure method by deep learning
Zechen Tang, Nianlong Zou, He Li +10
The combination of deep learning and ab initio materials calculations is emerging as a trending frontier of materials science research, with deep-learning density functional theory…
Neural-network Density Functional Theory Based on Variational Energy Minimization
Yang Li, Zechen Tang, Zezhou Chen +7
Deep-learning density functional theory (DFT) shows great promise to significantly accelerate material discovery and potentially revolutionize materials research. However, current…
AmbiGen: Generating Ambigrams from Pre-trained Diffusion Model
Boheng Zhao, Rana Hanocka, Raymond A. Yeh
Ambigrams are calligraphic designs that have different meanings depending on the viewing orientation. Creating ambigrams is a challenging task even for skilled artists, as it requi…