Publications
P. Virtanen, T. E. Oliphant, M. Haberland, et al., "SciPy 1.0 -- Fundamental Algorithms for Scientific Computing in Python" arXiv (2019) [arXiv]
R. T. McGibbon, A. G. Taube, A. G. Donchev, K. Siva, F. Hernández, C. Hargus, K.-H. Law, J. L. Klepeis, and D. E. Shaw, "Improving the accuracy of Møller-Plesset perturbation theory with neural networks" J. Chem. Phys. (2017) [doi]
P. Eastman, J. Swails, J. D. Chodera, R. T. McGibbon, Y. Zhao, K. A. Beauchamp, L.-P. Wang, A. C. Simmonett, M. P. Harrigan, B. R. Brooks and V. S. Pande, "OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics" PLOS Comput. Biol. (2017) [doi] [bioRxiv]
R. T. McGibbon, B. E. Husic and V. S. Pande, "Identification of simple reaction coordinates from complex dynamics" J. Chem. Phys. (2017) [doi] [arXiv]
M. P. Harrigan, M. M. Sultan, C. X. Hernández, B. E. Husic, P/ Eastman, C. R. Schwantes, K. A. Beauchamp, R. T. McGibbon and V. S. Pande, "MSMBuilder: Statistical Models for Biomolecular Dynamics" Biophys. J. (2017) [doi] [bioRxiv]
B. E. Husic, R. T. McGibbon and V. S. Pande, "Optimized parameter selection reveals trends in Markov state models for protein folding" J. Chem. Phys. (2016) [doi]
R. T. McGibbon, C. X. Hernández, M. P. Harrigan, S. Kearnes, M. M. Sultan, S. Jastrzebski, B. E. Husic, and V. S. Pande, J. Open Source Software (2016) [doi]
The Theano Development Team, R. Al-Rfou, G. Alain, et al., "Theano: A Python framework for fast computation of mathematical expressions" arXiv (2016) [arxiv]
L.-P. Wang, R. T. McGibbon, V. S. Pande and T. J. Martinez, "Automated Discovery and Refinement of Reactive Molecular Dynamics Pathways" J. Chem. Theory Comput. (2015) [doi] [pdf]
R. T. McGibbon and V. S. Pande, "Efficient maximum likelihood parameterization of continuous-time Markov processes" J. Chem. Phys. (2015) [doi] [pdf]
R. T. McGibbon, K. A. Beauchamp, M. P. Harrigan, C. Klein, J. M. Swails, C. X. Hernandez, C. R. Schwantes, L.-P. Wang, T. J. Lane and V. S. Pande, "MDTraj: a modern, open library for the analysis of molecular dynamics trajectories" Biophys J. (2015) [doi] [pdf]
L.-P. Wang, A. Titov, R. T. McGibbon, F. Liu, V. S. Pande, and T. J. Martinez, "Discovering chemistry with an ab initio nanoreactor" Nature Chem. (2014) [doi] [pdf]
R. T. McGibbon and V. S. Pande, "Variational cross-validation of slow dynamical modes in molecular kinetics" J. Chem. Phys. (2015) [doi] [pdf]
C. R. Schwantes, R. T. McGibbon, and V. S. Pande, "Perspective: Markov Models for Long-Timescale Biomolecular Dynamics" J. Chem. Phys. (2014) [doi] [pdf]
R. T. McGibbon, B. Ramsundar, M. M. Sultan, G. Kiss and V. S. Pande, "Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models" ICML (2014) [jmlr] [arXiv]
R. T. McGibbon, C. R. Schwantes, and V. S. Pande, "Statistical Model Selection for Markov Models of Biomolecular Dynamics" J. Phys. Chem B (2014) [doi] [pdf]
R. T. McGibbon and V. S. Pande, "Learning Kinetic Distance Metrics for Markov State Models of Protein Conformational Dynamics" J. Chem. Theory Comput. (2013) [doi] [pdf]
K. A. Beauchamp, R. T. McGibbon, Y. S. Lin and V. S. Pande, "Simple few-state models reveal hidden complexity in protein folding," Proc. Natl. Acad. Sci. U. S. A. (2012) [doi] [pdf]
A. J. Morris, R. T. McGibbon and A. B. Bocarsly, "Electrocatalytic Carbon Dioxide Activation: The Rate-Determining Step of Pyridinium-Catalyzed CO2 Reduction," ChemSusChem (2011) [doi] [pdf]