Page 100 - Read Online
P. 100

Page 22 of 23                         Li et al. J. Mater. Inf. 2025, 5, 43  https://dx.doi.org/10.20517/jmi.2025.17

                    30 Jun 2025)
               65.       Yan, B. Spin-orbit coupling: a relativistic effect. 2016. https://tms16.sciencesconf.org/data/pages/SOC_lecture1.pdf. (accessed 30 Jun
                    2025).
               66.       Inorganic Chemistry II review: 6.1 solid state structures. https://library.fiveable.me/inorganic-chemistry-ii/unit-6/solid-state-
                    structures/study-guide/qnl67GdXxyZ74VJP. (accessed 30 Jun 2025).
               67.       Gong, X.; Li, H.; Zou, N.; Xu, R.; Duan, W.; Xu, Y. General framework for E(3)-equivariant neural network representation of density
                    functional theory Hamiltonian. Nat. Commun. 2023, 14, 2848.  DOI  PubMed  PMC
               68.       Schütt, K. T.; Gastegger, M.; Tkatchenko, A.; Müller, K. R.; Maurer, R. J. Unifying machine learning and quantum chemistry with a
                    deep neural network for molecular wavefunctions. Nat. Commun. 2019, 10, 5024.  DOI  PubMed  PMC
               69.       Unke, O. T.; Bogojeski, M.; Gastegger, M.; Geiger, M.; Smidt, T.; Muller, K. R. SE(3)-equivariant prediction of molecular
                    wavefunctions and electronic densities. In Advances in Neural Information Processing Systems 34 (NeurIPS 2021). 2021. https://
                    proceedings.neurips.cc/paper/2021/hash/78f1893678afbeaa90b1fa01b9cfb860-Abstract.html. (accessed 30 Jun 2025).
               70.       Thomas, N.; Smidt, T.; Kearnes, S.; et al. Tensor field networks: rotation- and translation-equivariant neural networks for 3D point
                    clouds. arXiv 2018, arXiv:1802.08219. https://doi.org/10.48550/arXiv.1802.08219. (accessed 30 Jun 2025)
               71.       Passaro, S.; Zitnick, C. L. Reducing SO(3) convolutions to SO(2) for efficient equivariant GNNs. arXiv 2023, arXiv:2302.03655.
                    https://doi.org/10.48550/arXiv.2302.03655. (accessed 30 Jun 2025)
               72.       Wang, Y.; Li, H.; Tang, Z.; et al. DeepH-2: enhancing deep-learning electronic structure via an equivariant local-coordinate
                    transformer. arXiv 2024, arXiv:2401.17015. https://doi.org/10.48550/arXiv.2401.17015. (accessed 30 Jun 2025)
               73.       Zhouyin, Z.; Gan, Z.; Liu, M.; Pandey, S. K.; Zhang, L.; Gu, Q. Learning local equivariant representations for quantum operators.
                    arXiv 2024, arXiv:2407.06053. https://doi.org/10.48550/arXiv.2407.06053. (accessed 30 Jun 2025)
               74.       Li, Y.; Xia, Z.; Huang, L.; et al. Enhancing the scalability and applicability of Kohn-Sham Hamiltonians for molecular systems. arXiv
                    2025, arXiv:2502.19227. https://doi.org/10.48550/arXiv.2502.19227. (accessed 30 Jun 2025)
               75.       Yin, S.; Pan, X.; Wang, F.; He, L. TraceGrad: a framework learning expressive SO(3)-equivariant non-linear representations for
                    electronic-structure Hamiltonian prediction. arXiv 2024, arXiv:2405.05722. https://doi.org/10.48550/arXiv.2405.05722. (accessed 30
                    Jun 2025)
               76.       Yu, H.; Xu, Z.; Qian, X.; Qian, X.; Ji, S. Efficient and equivariant graph networks for predicting quantum Hamiltonian. arXiv 2023,
                    arXiv:2306.04922. https://doi.org/10.48550/arXiv.2306.04922. (accessed 30 Jun 2025)
               77.       Zhong, Y.; Yu, H.; Su, M.; Gong, X.; Xiang, H. Transferable equivariant graph neural networks for the Hamiltonians of molecules
                    and solids. npj. Comput. Mater. 2023, 9, 1130.  DOI
               78.       Tang, Z.; Li, H.; Lin, P.; et al. A deep equivariant neural network approach for efficient hybrid density functional calculations. Nat.
                    Commun. 2024, 15, 8815.  DOI  PubMed  PMC
               79.       Li, H.; Tang, Z.; Fu, J.; et al. Deep-learning density functional perturbation theory. Phys. Rev. Lett. 2024, 132, 096401.  DOI
               80.       Yin, S.; Pan, X.; Zhu, X.; et al. Towards harmonization of SO(3)-equivariance and expressiveness: a hybrid deep learning framework
                    for electronic-structure Hamiltonian prediction. Mach. Learn. Sci. Technol. 2024, 5, 045038.  DOI
               81.       Li, H.; Tang, Z.; Gong, X.; Zou, N.; Duan, W.; Xu, Y. Deep-learning electronic-structure calculation of magnetic superstructures.
                    Nat. Comput. Sci. 2023, 3, 321-7.  DOI  PubMed  PMC
               82.       Gong, X.; Louie, S. G.; Duan, W.; Xu, Y. Generalizing deep learning electronic structure calculation to the plane-wave basis. Nat.
                    Comput. Sci. 2024, 4, 752-60.  DOI  PubMed  PMC
               83.       Wang, Y.; Li, Y.; Tang, Z.; et al. Universal materials model of deep-learning density functional theory Hamiltonian. Sci. Bull. 2024,
                    69, 2514-21.  DOI
               84.       Tang, H.; Xiao, B.; He, W.; et al. Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning.
                    Nat. Comput. Sci. 2025, 5, 144-54.  DOI
               85.       Zhong, Y.; Liu, S.; Zhang, B.; et al. Accelerating the calculation of electron-phonon coupling strength with machine learning. Nat.
                    Comput. Sci. 2024, 4, 615-25.  DOI
               86.       Ma, Y.; Yu, H.; Zhong, Y.; Chen, S.; Gong, X.; Xiang, H. Transferable machine learning approach for predicting electronic structures
                    of charged defects. Appl. Phys. Lett. 2025, 126, 044103.  DOI
               87.       Gu, Q.; Zhouyin, Z.; Pandey, S. K.; Zhang, P.; Zhang, L.; E, W. Deep learning tight-binding approach for large-scale electronic
                    simulations at finite temperatures with ab initio accuracy. Nat. Commun. 2024, 15, 6772.  DOI  PubMed  PMC
               88.       Zheng, H.; Sivonxay, E.; Christensen, R.; et al. The ab initio non-crystalline structure database: empowering machine learning to
                    decode diffusivity. npj. Comput. Mater. 2024, 10, 1469.  DOI
               89.       Huang, P.; Lukin, R.; Faleev, M.; et al. Unveiling the complex structure-property correlation of defects in 2D materials based on high
                    throughput datasets. npj. 2D. Mater. Appl. 2023, 7, 369.  DOI
               90.       Zhou, Z.; Zhou, Y.; He, Q.; Ding, Z.; Li, F.; Yang, Y. Machine learning guided appraisal and exploration of phase design for high
                    entropy alloys. npj. Comput. Mater. 2019, 5, 265.  DOI
               91.       Chen, Y.; Zhang, L.; Wang, H.; E, W. DeePKS: a comprehensive data-driven approach toward chemically accurate density functional
                    theory. J. Chem. Theory. Comput. 2021, 17, 170-81.  DOI
               92.       Ou, Q.; Tuo, P.; Li, W.; Wang, X.; Chen, Y.; Zhang, L. DeePKS model for halide perovskites with the accuracy of a hybrid
                    functional. J. Phys. Chem. C. 2023, 127, 18755-64.  DOI
               93.       Kakkad, J.; Jannu, J.; Sharma, K.; Aggarwal, C.; Medya, S. A survey on explainability of graph neural networks. arXiv 2023,
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