| OpenMM 7: Rapid development of high performance algorithms for molecular dynamics P Eastman, J Swails, JD Chodera, RT McGibbon, Y Zhao, KA Beauchamp, ... PLoS computational biology 13 (7), e1005659, 2017 | 2184 | 2017 |
| MDTraj: a modern open library for the analysis of molecular dynamics trajectories RT McGibbon, KA Beauchamp, MP Harrigan, C Klein, JM Swails, ... Biophysical journal 109 (8), 1528-1532, 2015 | 1912 | 2015 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv e-prints, arXiv: 1605.02688, 2016 | 926 | 2016 |
| Discovering chemistry with an ab initio nanoreactor LP Wang, A Titov, R McGibbon, F Liu, VS Pande, TJ Martínez Nature chemistry 6 (12), 1044-1048, 2014 | 391 | 2014 |
| MSMBuilder: statistical models for biomolecular dynamics MP Harrigan, MM Sultan, CX Hernández, BE Husic, P Eastman, ... Biophysical journal 112 (1), 10-15, 2017 | 271 | 2017 |
| Electrocatalytic Carbon Dioxide Activation: The Rate‐Determining Step of Pyridinium‐Catalyzed CO2 Reduction AJ Morris, RT McGibbon, AB Bocarsly ChemSusChem 4 (2), 191-196, 2011 | 245 | 2011 |
| Theano: A Python framework for fast computation of mathematical expressions TTD Team, R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, ... arXiv preprint arXiv:1605.02688, 2016 | 215 | 2016 |
| Variational cross-validation of slow dynamical modes in molecular kinetics RT McGibbon, VS Pande The Journal of chemical physics 142 (12), 2015 | 208 | 2015 |
| Simple few-state models reveal hidden complexity in protein folding KA Beauchamp, R McGibbon, YS Lin, VS Pande Proceedings of the National Academy of Sciences 109 (44), 17807-17813, 2012 | 182 | 2012 |
| Automated discovery and refinement of reactive molecular dynamics pathways LP Wang, RT McGibbon, VS Pande, TJ Martinez Journal of chemical theory and computation 12 (2), 638-649, 2016 | 134 | 2016 |
| Improving the accuracy of Mřller-Plesset perturbation theory with neural networks RT McGibbon, AG Taube, AG Donchev, K Siva, F Hernández, C Hargus, ... The Journal of chemical physics 147 (16), 2017 | 119 | 2017 |
| Identification of simple reaction coordinates from complex dynamics RT McGibbon, BE Husic, VS Pande The Journal of Chemical Physics 146 (4), 2017 | 102 | 2017 |
| Perspective: Markov models for long-timescale biomolecular dynamics CR Schwantes, RT McGibbon, VS Pande The Journal of chemical physics 141 (9), 2014 | 100 | 2014 |
| Optimized parameter selection reveals trends in Markov state models for protein folding BE Husic, RT McGibbon, MM Sultan, VS Pande The Journal of chemical physics 145 (19), 2016 | 77 | 2016 |
| Quantum chemical benchmark databases of gold-standard dimer interaction energies AG Donchev, AG Taube, E Decolvenaere, C Hargus, RT McGibbon, ... Scientific data 8 (1), 55, 2021 | 69 | 2021 |
| Learning kinetic distance metrics for Markov state models of protein conformational dynamics RT McGibbon, VS Pande Journal of chemical theory and computation 9 (7), 2900-2906, 2013 | 58 | 2013 |
| Theano: A Python framework for fast computation of mathematical expressions. arXiv R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv preprint arXiv:1605.02688 10, 2016 | 52 | 2016 |
| Osprey: Hyperparameter optimization for machine learning RT McGibbon, CX Hernández, MP Harrigan, S Kearnes, MM Sultan, ... Journal of Open Source Software 1 (5), 34, 2016 | 46 | 2016 |
| Efficient maximum likelihood parameterization of continuous-time Markov processes RT McGibbon, VS Pande The Journal of chemical physics 143 (3), 2015 | 40 | 2015 |
| Statistical model selection for Markov models of biomolecular dynamics RT McGibbon, CR Schwantes, VS Pande The Journal of Physical Chemistry B 118 (24), 6475-6481, 2014 | 31 | 2014 |
