| Δ-machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD (T) level of theory A Nandi, C Qu, PL Houston, R Conte, JM Bowman The Journal of Chemical Physics 154 (5), 2021 | 104 | 2021 |
| A machine learning approach for prediction of rate constants PL Houston, A Nandi, JM Bowman The Journal of Physical Chemistry Letters 10 (17), 5250-5258, 2019 | 53 | 2019 |
| q-AQUA: A many-body CCSD (T) water potential, including four-body interactions, demonstrates the quantum nature of water from clusters to the liquid phase Q Yu, C Qu, PL Houston, R Conte, A Nandi, JM Bowman The Journal of Physical Chemistry Letters 13 (22), 5068-5074, 2022 | 48 | 2022 |
| Using Gradients in Permutationally Invariant Polynomial Potential Fitting: A Demonstration for CH4 Using as Few as 100 Configurations A Nandi, C Qu, JM Bowman Journal of Chemical Theory and Computation 15 (5), 2826-2835, 2019 | 47 | 2019 |
| Breaking the coupled cluster barrier for machine-learned potentials of large molecules: The case of 15-atom acetylacetone C Qu, PL Houston, R Conte, A Nandi, JM Bowman The Journal of Physical Chemistry Letters 12 (20), 4902-4909, 2021 | 46 | 2021 |
| Full and fragmented permutationally invariant polynomial potential energy surfaces for trans and cis N-methyl acetamide and isomerization saddle points A Nandi, C Qu, JM Bowman The Journal of Chemical Physics 151 (8), 2019 | 35 | 2019 |
| Toward an accurate and inexpensive estimation of CCSD (T)/CBS binding energies of large water clusters N Sahu, G Singh, A Nandi, SR Gadre The Journal of Physical Chemistry A 120 (28), 5706-5714, 2016 | 32 | 2016 |
| Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to … PL Houston, C Qu, A Nandi, R Conte, Q Yu, JM Bowman The Journal of Chemical Physics 156 (4), 2022 | 31 | 2022 |
| δ-machine learned potential energy surfaces and force fields JM Bowman, C Qu, R Conte, A Nandi, PL Houston, Q Yu Journal of Chemical Theory and Computation 19 (1), 1-17, 2022 | 29 | 2022 |
| A CCSD (T)-based 4-body potential for water A Nandi, C Qu, PL Houston, R Conte, Q Yu, JM Bowman The Journal of Physical Chemistry Letters 12 (42), 10318-10324, 2021 | 28 | 2021 |
| A Machine Learning Approach for Rate Constants. II. Clustering, Training, and Predictions for the O(3P) + HCl → OH + Cl Reaction A Nandi, JM Bowman, P Houston The Journal of Physical Chemistry A 124 (28), 5746-5755, 2020 | 28 | 2020 |
| The MD17 datasets from the perspective of datasets for gas-phase “small” molecule potentials JM Bowman, C Qu, R Conte, A Nandi, PL Houston, Q Yu The Journal of Chemical Physics 156 (24), 2022 | 22 | 2022 |
| Breaking the bottleneck: Use of molecular tailoring approach for the estimation of binding energies at MP2/CBS limit for large water clusters G Singh, A Nandi, SR Gadre The Journal of Chemical Physics 144 (10), 2016 | 22 | 2016 |
| PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials PL Houston, C Qu, Q Yu, R Conte, A Nandi, JK Li, JM Bowman The Journal of Chemical Physics 158 (4), 2023 | 16 | 2023 |
| A Δ-machine learning approach for force fields, illustrated by a CCSD (T) 4-body correction to the MB-pol water potential C Qu, Q Yu, R Conte, PL Houston, A Nandi, JM Bomwan Digital Discovery 1 (5), 658-664, 2022 | 14 | 2022 |
| Ring-polymer instanton tunneling splittings of tropolone and isotopomers using a δ-machine learned ccsd (t) potential: Theory and experiment shake hands A Nandi, G Laude, SS Khire, ND Gurav, C Qu, R Conte, Q Yu, S Li, ... Journal of the American Chemical Society 145 (17), 9655-9664, 2023 | 13 | 2023 |
| MULTIMODE calculations of vibrational spectroscopy and 1d interconformer tunneling dynamics in Glycine using a full-dimensional potential energy surface C Qu, PL Houston, R Conte, A Nandi, JM Bowman The Journal of Physical Chemistry A 125 (24), 5346-5354, 2021 | 13 | 2021 |
| Two-layer Gaussian-based MCTDH study of the S1← S vibronic absorption spectrum of formaldehyde using multiplicative neural network potentials W Koch, M Bonfanti, P Eisenbrandt, A Nandi, B Fu, J Bowman, D Tannor, ... The Journal of Chemical Physics 151 (6), 2019 | 12 | 2019 |
| Quantum Calculations on a New CCSD(T) Machine-Learned Potential Energy Surface Reveal the Leaky Nature of Gas-Phase Trans and Gauche Ethanol … A Nandi, R Conte, C Qu, PL Houston, Q Yu, JM Bowman Journal of Chemical Theory and Computation 18 (9), 5527-5538, 2022 | 11 | 2022 |
| Quasiclassical simulations based on cluster models reveal vibration-facilitated roaming in the isomerization of CO adsorbed on NaCl A Nandi, P Zhang, J Chen, H Guo, JM Bowman Nature Chemistry 13 (3), 249-254, 2021 | 11 | 2021 |
