| A Status Report on “Gold Standard” Machine-Learned Potentials for Water Q Yu, C Qu, PL Houston, A Nandi, P Pandey, R Conte, JM Bowman The Journal of Physical Chemistry Letters 14 (36), 8077-8087, 2023 | 6 | 2023 |
| Machine learning classification can significantly reduce the cost of calculating the Hamiltonian matrix in CI calculations C Qu, PL Houston, Q Yu, R Conte, P Pandey, A Nandi, JM Bowman Journal of Chemical Physics 159 (7), 071101-6, 2023 | 6 | 2023 |
| Classical and Quantum Dynamical Manifestations of Index- Saddles: Concerted Versus Sequential Reaction Mechanisms P Pandey, S Naik, S Keshavamurthy Regular and Chaotic Dynamics 26 (2), 165-182, 2021 | 5 | 2021 |
| No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials PL Houston, C Qu, Q Yu, P Pandey, R Conte, A Nandi, JM Bowman arXiv preprint arXiv:2401.09316, 2024 | 2* | 2024 |
| Machine learning software to learn negligible elements of the Hamiltonian matrix C Qu, PL Houston, Q Yu, P Pandey, R Conte, A Nandi, JM Bowman Artificial Intelligence Chemistry 1 (2), 100025, 2023 | 2 | 2023 |
| Influence of low frequency modes on dynamical concertedness in double proton transfer dynamics P Pandey, S Naik, S Keshavamurthy Communications in Nonlinear Science and Numerical Simulation 109, 106326, 2022 | 2 | 2022 |
| Diffusion Monte Carlo and PIMD calculations of radial distribution functions using an updated CCSD(T) potential for CH5+ C Qu, Q Yu, PL Houston, P Pandey, R Conte, A Nandi, JM Bowman Molecular Physics, e2262058, 2023 | 1 | 2023 |
| Dynamic matching—Revisiting the Carpenter model P Pandey, S Keshavamurthy Journal of Physical Organic Chemistry 35 (11), e4404, 2022 | 1 | 2022 |
| Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H3O2– P Pandey, M Arandhara, PL Houston, C Qu, R Conte, JM Bowman, ... The Journal of Physical Chemistry A, 2024 | | 2024 |
| No Headache for PIPs: A PIP Potential for Aspirin Runs Much Faster and with Similar Precision Than Other Machine-Learned Potentials PL Houston, C Qu, Q Yu, P Pandey, R Conte, A Nandi, JM Bowman Journal of Chemical Theory and Computation, 2024 | | 2024 |
| Formic Acid–Ammonia Heterodimer: A New Δ-Machine Learning CCSD (T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer PL Houston, C Qu, Q Yu, P Pandey, R Conte, A Nandi, JM Bowman, ... Journal of Chemical Theory and Computation, 2024 | | 2024 |
| Assessing PIP and sGDML Potential Energy Surfaces for H3O2 P Pandey, M Arandhara, PL Houston, C Qu, R Conte, JM Bowman, ... arXiv preprint arXiv:2402.11158, 2024 | | 2024 |
| Ab Initio Potential Energy Surface for NaCl–H2 with Correct Long-Range Behavior P Pandey, C Qu, A Nandi, Q Yu, PL Houston, R Conte, JM Bowman The Journal of Physical Chemistry A, 2024 | | 2024 |
| A New A Priori Method to Avoid Calculation of Negligible Hamiltonian Matrix Elements in CI Calculation PL Houston, C Qu, Q Yu, P Pandey, R Conte, A Nandi, JM Bowman The Journal of Physical Chemistry A 128 (2), 479-487, 2024 | | 2024 |
| Communication J Chem Phys: Machine Learning Classification can Significantly Reduce the Cost of Calculating the Hamiltonian Matrix in CI Calculations C Qu, P Houston, Q Yu, R Conte, P Pandey, A Nandi, J Bowman | | 2023 |
