References¶

The methods Trails-MD builds on, with primary references. Cite the relevant ones alongside Trails-MD (see CITATION.cff).

Markov state models¶

Prinz, J.-H. et al. Markov models of molecular kinetics: Generation and validation. J. Chem. Phys. 134, 174105 (2011).
Husic, B. E. & Pande, V. S. Markov state models: From an art to a science. J. Am. Chem. Soc. 140, 2386–2396 (2018).
Röblitz, S. & Weber, M. Fuzzy spectral clustering by PCCA+. Adv. Data Anal. Classif. 7, 147–179 (2013). (PCCA+ metastable decomposition.)

Pérez-Hernández, G. et al. Identification of slow molecular order parameters for Markov model construction (TICA). J. Chem. Phys. 139, 015102 (2013).
Schwantes, C. R. & Pande, V. S. Improvements in Markov state model construction reveal many non-native interactions in the folding of NTL9. J. Chem. Theory Comput. 9, 2000–2009 (2013). (TICA.)
Bonati, L., Piccini, G. & Parrinello, M. Deep learning the slow modes for rare events sampling (Deep-TICA). PNAS 118, e2113533118 (2021).
Time-lagged (variational) autoencoders: Wehmeyer, C. & Noé, F. Time-lagged autoencoders. J. Chem. Phys. 148, 241703 (2018).

Hoffmann, M. et al. Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn.: Sci. Technol. 3, 015009 (2022).
Eastman, P. et al. OpenMM 8. J. Phys. Chem. B 128, 109–116 (2024).
Michaud-Agrawal, N. et al. MDAnalysis: A toolkit for the analysis of molecular dynamics simulations. J. Comput. Chem. 32, 2319–2327 (2011).