| Interpolation and extrapolation of global potential energy surfaces for polyatomic systems by Gaussian processes with composite kernels J Dai, RV Krems Journal of Chemical Theory and Computation 16 (3), 1386-1395, 2020 | 45 | 2020 |
| Machine learning corrected quantum dynamics calculations A Jasinski, J Montaner, RC Forrey, BH Yang, PC Stancil, N Balakrishnan, ... Physical Review Research 2 (3), 032051, 2020 | 21 | 2020 |
| Quantum Gaussian process model of potential energy surface for a polyatomic molecule J Dai, RV Krems The Journal of Chemical Physics 156 (18), 2022 | 9 | 2022 |
| Neural network Gaussian processes as efficient models of potential energy surfaces for polyatomic molecules J Dai, RV Krems Machine Learning: Science and Technology 4 (4), 045027, 2023 | 5 | 2023 |
| GFlowNets for Hamiltonian decomposition in groups of compatible operators IL Huidobro-Meezs, J Dai, G Rabusseau, RA Vargas-Hernández arXiv preprint arXiv:2410.16041, 2024 | | 2024 |
| Benchmarking of quantum fidelity kernels for Gaussian process regression X Guo, J Dai, RV Krems Machine Learning: Science and Technology 5 (3), 035081, 2024 | | 2024 |
| Applications of classical and quantum machine learning for quantum problems J Dai University of British Columbia, 2023 | | 2023 |
| Gausian processes for system-agnostic construction of high-dimensional PES with sparse ab initio data J Dai, H Sugisawa, T Ida, R Krems APS Division of Atomic, Molecular and Optical Physics Meeting Abstracts 2020 …, 2020 | | 2020 |
