2020/06/16 by John D. Russo, Russo, John D., Jeremy Copperman +3
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Computation (stat.CO) #Computational Physics (physics.comp-ph) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Protein Structure and Dynamics #Quantitative Methods (q-bio.QM) #Spectroscopy and Quantum Chemical Studies #Statistical Mechanics and Entropy
paper · pdf · doi:10.48550/arxiv.2006.09451
openalex publication_date 2020/06/16 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
We present two algorithms by which a set of short, unbiased trajectories can be iteratively reweighted to obtain various observables. The first algorithm estimates the stationary (steady state) distribution of a system by iteratively reweighting the trajectories based on the average probability in each state. The algorithm applies to equilibrium or non-equilibrium steady states, exploiting the `left' stationarity of the distribution under dynamics -- i.e., in a discrete setting, when the column vector of probabilities is multiplied by the transition matrix expressed as a left stochastic matrix. The second procedure relies on the `right' stationarity of the committor (splitting probability) expressed as a row vector. The algorithms are unbiased, do not rely on computing transition matrices, and make no Markov assumption about discretized states. Here, we apply the procedures to a one-dimensional double-well potential, and to a 208μs atomistic Trp-cage folding trajectory from D.E. Shaw Research.