44 citations · 90 across the 9 of their papers we have counts for
9 papers
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…
Electric circuit analog of Landau-Zener tunneling using time-varying elements
Enhong Cheng, Zheng Lian, Zezhou Chen +1
Landau-Zener tunneling (LZT) is a fundamental dynamical phenomenon, ubiquitous in various quantum systems. Here, we propose a time-varying electric circuit to address the question…
Revisiting dissipation-driven phase transition in a Josephson junction
Diego Subero, Yu-Cheng Chang, Miguel Monteiro +2
Despite extensive experimental and theoretical work over several decades, Schmid-Bulgadaev quantum phase transition remains a subject of debate. Here we revisit this problem by per…
Field theory of non-Hermitian disordered systems
Ze Chen, Kohei Kawabata, Anish Kulkarni +1
The interplay between non-Hermiticity and disorder gives rise to unique universality classes of Anderson transitions. Here, we develop a field-theoretical description of non-Hermit…
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…
Universal materials model of deep-learning density functional theory Hamiltonian
Yuxiang Wang, Yang Li, Zechen Tang +14
Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challeng…
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…
DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer
Yuxiang Wang, He Li, Zechen Tang +6
Deep-learning electronic structure calculations show great potential for revolutionizing the landscape of computational materials research. However, current neural-network architec…