6 papers
NextCrystal: a Symmetry-Driven Generative Framework for Crystal Structure Prediction
Jinming Mu, Lixin He, Xudong Zhu +1
Crystal structure prediction (CSP), which aims to predict the 3D atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understandi…
Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials
Shi Yin, Zujian Dai, Xinyang Pan +1
Deep learning methods for electronic-structure Hamiltonian prediction has offered significant computational efficiency advantages over traditional DFT methods, yet the diversity of…
Dissecting the moat regime at low energies I: Renormalization and the phase structure
Fabian Rennecke, Shi Yin
Dense QCD matter can feature a moat regime, where the static energy of mesons is minimal at nonzero momentum. Valuable insights into this regime can be gained using low-energy mode…
TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction
Shi Yin, Xinyang Pan, Fengyan Wang +1
We propose a framework to combine strong non-linear expressiveness with strict SO(3)-equivariance in prediction of the electronic-structure Hamiltonian, by exploring the mathematic…
GPU Acceleration of Numerical Atomic Orbitals-Based Density Functional Theory Algorithms within the ABACUS package
Haochong Zhang, Zichao Deng, Yu Liu +4
With the fast developments of high-performance computing, first-principles methods based on quantum mechanics play a significant role in materials research, serving as fundamental…
Towards Harmonization of SO(3)-Equivariance and Expressiveness: a Hybrid Deep Learning Framework for Electronic-Structure Hamiltonian Prediction
Shi Yin, Xinyang Pan, Xudong Zhu +4
Deep learning for predicting the electronic-structure Hamiltonian of quantum systems necessitates satisfying the covariance laws, among which achieving SO(3)-equivariance without s…