3 papers
cs.LG2025
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…
cs.LG2024
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…
physics.comp-ph2024
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…