Jiace Sun

Jiace Sun

Ph.D student

Publications

  1. (preparing) Sun, J., Ren, J., etc. (2024). Machine-learned sum-of-products potential energy surfaces for density matrix renormalization group simulations of molecular quantum dynamics.

  2. Sun, J., Cheng, L. & Zhang, S.-X. (2024). Stabilizer ground states: theory, algorithms and applications. arXiv preprint arXiv:2403.08441. (Note: First author and corresponding author) [link]

  3. Kamakari, H., Sun, J., Li, Y., Thio, J. J., Gujarati, T. P., Fisher, M., Motta, M. & Minnich, A. J. (2024). Experimental demonstration of scalable cross-entropy benchmarking to detect measurement-induced phase transitions on a superconducting quantum processor. arXiv preprint arXiv:2403.00938. [link]

  4. Sun, J., Cheng, L. & Li, W. (2024). Towards chemical accuracy with shallow quantum circuits: A Clifford-based Hamiltonian engineering approach. Journal of Chemical Theory and Computation. [link]

  5. Sun, J. & Minnich, A. J. (2023). Transport and noise of hot electrons in GaAs using a semi-analytical model of two-phonon polar optical phonon scattering. Physical Review B 107, 205201. [link]

  6. Zhang, S.X., Allcock, J., Wan, Z.Q., Liu, S., Sun, J., Yu, H., Yang, X.H., Qiu, J., Ye, Z., Chen, Y.Q. & Lee, C.K. (2023). TensorCircuit: a quantum software framework for the NISQ era. Quantum, 7, 912. [link]

  7. Cheng, L., Sun, J., Emiliano Deustua, J., Bhethanabotla, V. C. & Miller III, T. F. (2022). Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression. The Journal of Chemical Physics, 157, 154105. [link]

  8. Sun, J., Cheng, L., & Miller III, T. F. (2022). Molecular dipole moment learning via rotationally equivariant Gaussian process regression with derivatives in molecular-orbital-based machine learning. The Journal of Chemical Physics, 157, 104109. [link]

  9. Cheng, L., Sun, J. & Miller III, T.F. (2022). Accurate molecular-orbital-based machine learning energies via unsupervised clustering of chemical space. Journal of Chemical Theory and Computation, 18, 8, 4826–4835. [link]

  10. Lu, F., Cheng, L., DiRisio, R.J., Finney, J.M., Boyer, M.A., Moonkaen, P., Sun, J., Lee, S.J., Deustua, J.E., Miller III, T.F. & McCoy, A.B. (2022). Fast near ab initio potential energy surfaces using machine learning. The Journal of Physical Chemistry A, 126(25), 4013-4024. [link]

  11. Cheng, P. S., Sun, J., Sun, S. N., Choi, A. Y., & Minnich, A. J. (2022). High-field transport and hot electron noise in GaAs from first principles: role of two-phonon scattering. Physical Review B, 106, 245201. [link]

  12. Gui, X., Fan, W., Sun, J., & Li, Y. (2022). New stable and fast ring-polymer molecular dynamics for calculating bimolecular rate coefficients with example of OH+CH4. Journal of Chemical Theory and Computation, 18, 9, 5203–5212. [link]

  13. Sun, J., Cheng, L., & Miller III, T. F. (2021). Molecular energy learning using alternative blackbox matrix-matrix multiplication algorithm for exact Gaussian process. NeurIPS 2021 AI for Science Workshop. [link]

  14. Husch, T., Sun, J., Cheng, L., Lee, S. J., & Miller III, T. F. (2021). Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states. The Journal of Chemical Physics, 154, 064108. [link]

  15. Rosa-Raíces, J. L.$^{\star}$, Sun, J.$^{\star}$, Bou-Rabee, N., & Miller III, T. F. (2021). A generalized class of strongly stable and dimension-free T-RPMD integrators. The Journal of Chemical Physics, 154, 024106. [link]

  16. Sun, J.$^{\star}$, Feng, S.$^{\star}$, Wang, X., Zhang, G., Luo, Y., & Jiang, J. (2020). Regulation of electronic structure of graphene nanoribbon by tuning long-range dopant–dopant coupling at distance of tens of nanometers. The Journal of Physical Chemistry Letters, 11(16), 6907-6913. [link]