| Discrete‐variable representations and their utilization JC Light, T Carrington Jr Advances in Chemical Physics 114, 263-310, 2000 | 1013 | 2000 |
| Encyclopedia of computational chemistry PR Schleyer (No Title), 1998 | 594 | 1998 |
| Variational quantum approaches for computing vibrational energies of polyatomic molecules JM Bowman, T Carrington, HD Meyer Molecular Physics 106 (16-18), 2145-2182, 2008 | 474 | 2008 |
| A general discrete variable method to calculate vibrational energy levels of three‐and four‐atom molecules MJ Bramley, T Carrington Jr The Journal of chemical physics 99 (11), 8519-8541, 1993 | 434 | 1993 |
| The discrete variable representation of a triatomic Hamiltonian in bond length–bond angle coordinates H Wei, T Carrington Jr The Journal of chemical physics 97 (5), 3029-3037, 1992 | 393 | 1992 |
| Reaction surface description of intramolecular hydrogen atom transfer in malonaldehyde T Carrington Jr, WH Miller The Journal of chemical physics 84 (8), 4364-4370, 1986 | 305 | 1986 |
| Fermi resonances and local modes in water, hydrogen sulfide, and hydrogen selenide L Halonen, T Carrington Jr The Journal of chemical physics 88 (7), 4171-4185, 1988 | 281 | 1988 |
| A random-sampling high dimensional model representation neural network for building potential energy surfaces S Manzhos, T Carrington The Journal of chemical physics 125 (8), 2006 | 276 | 2006 |
| Efficient calculation of highly excited vibrational energy levels of floppy molecules: The band origins of H+3 up to 35 000 cm−1 MJ Bramley, JW Tromp, T Carrington Jr, GC Corey The Journal of chemical physics 100 (9), 6175-6194, 1994 | 240 | 1994 |
| Neural network potential energy surfaces for small molecules and reactions S Manzhos, T Carrington Jr Chemical Reviews 121 (16), 10187-10217, 2020 | 231 | 2020 |
| Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy A Kamath, RA Vargas-Hernández, RV Krems, T Carrington, S Manzhos The Journal of chemical physics 148 (24), 2018 | 215 | 2018 |
| A nested molecule-independent neural network approach for high-quality potential fits S Manzhos, X Wang, R Dawes, T Carrington The Journal of Physical Chemistry A 110 (16), 5295-5304, 2006 | 213 | 2006 |
| A contracted basis-Lanczos calculation of vibrational levels of methane: Solving the Schrödinger equation in nine dimensions XG Wang, T Carrington Jr The Journal of chemical physics 119 (1), 101-117, 2003 | 212 | 2003 |
| Neural network‐based approaches for building high dimensional and quantum dynamics‐friendly potential energy surfaces Sergei Manzhos, Richard Dawes, Tucker Carrington International Journal of Quantum Chemistry 115 (16), 1012-1020, 2015 | 211 | 2015 |
| Vinylidene: Potential energy surface and unimolecular reaction dynamics T Carrington Jr, LM Hubbard, HF Schaefer III, WH Miller The Journal of chemical physics 80 (9), 4347-4354, 1984 | 203 | 1984 |
| Using neural networks to represent potential surfaces as sums of products S Manzhos, T Carrington The Journal of chemical physics 125 (19), 2006 | 196 | 2006 |
| Reaction surface Hamiltonian for the dynamics of reactions in polyatomic systems T Carrington Jr, WH Miller The Journal of chemical physics 81 (9), 3942-3950, 1984 | 177 | 1984 |
| A general framework for discrete variable representation basis sets RG Littlejohn, M Cargo, T Carrington Jr, KA Mitchell, B Poirier The Journal of chemical physics 116 (20), 8691-8703, 2002 | 170 | 2002 |
| Vibrational energy levels of CH5+ XG Wang, T Carrington The Journal of chemical physics 129 (23), 2008 | 163 | 2008 |
| New ideas for using contracted basis functions with a Lanczos eigensolver for computing vibrational spectra of molecules with four or more atoms XG Wang, T Carrington Jr The Journal of chemical physics 117 (15), 6923-6934, 2002 | 158 | 2002 |
