A machine learning tool to efficiently calculate electron–phonon coupling
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Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s43588-024-00680-x.pdf
Reference6 articles.
1. Giustino, F. Electron-phonon interactions from first principles. Rev. Mod. Phys. 89, 015003 (2017). A review article that presents the theory of electron–phonon interactions in solids.
2. Schütt, K. T. et al. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. Nat. Commun. 10, 5024 (2019). This paper presents a deep learning framework for predicting the electronic Hamiltonian in a local basis of molecular atomic orbitals.
3. Li, H. et al. Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation. Nat. Comput. Sci. 2, 367–377 (2022). This paper develops a deep neural network approach to represent the DFT Hamiltonian of crystalline materials.
4. Zhong, Y. et al. Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids. npj Comput. Mater. 9, 182 (2023). This paper develops a transferable equivariant graph neural network for the Hamiltonians of molecules and solids.
5. Ma, J., Nissimagoudar, A. S. & Li, W. First-principles study of electron and hole mobilities of Si and GaAs. Phys. Rev. B 97, 045201 (2018). A theoretical study on the carrier mobility of GaAs in comparison with experimental and other theoretical works.
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