PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models
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- 作者：Martin K. Scherer ; Benjamin Trendelkamp-Schroer ; Fabian Paul ; Guillermo P茅rez-Hern谩ndez ; Moritz Hoffmann ; Nuria Plattner ; Christoph Wehmeyer ; Jan-Hendrik Prinz ; Frank No茅
- 刊名：Journal of Chemical Theory and Computation
- 出版年：2015
- 出版时间：November 10, 2015
- 年：2015
- 卷：11
- 期：11
- 页码：5525-5542
- 全文大小：1088K
- ISSN：1549-9626
文摘
Markov (state) models (MSMs) and related models of molecular kinetics have recently received a surge of interest as they can systematically reconcile simulation data from either a few long or many short simulations and allow us to analyze the essential metastable structures, thermodynamics, and kinetics of the molecular system under investigation. However, the estimation, validation, and analysis of such models is far from trivial and involves sophisticated and often numerically sensitive methods. In this work we present the open-source Python package PyEMMA (http://pyemma.org) that provides accurate and efficient algorithms for kinetic model construction. PyEMMA can read all common molecular dynamics data formats, helps in the selection of input features, provides easy access to dimension reduction algorithms such as principal component analysis (PCA) and time-lagged independent component analysis (TICA) and clustering algorithms such as k-means, and contains estimators for MSMs, hidden Markov models, and several other models. Systematic model validation and error calculation methods are provided. PyEMMA offers a wealth of analysis functions such that the user can conveniently compute molecular observables of interest. We have derived a systematic and accurate way to coarse-grain MSMs to few states and to illustrate the structures of the metastable states of the system. Plotting functions to produce a manuscript-ready presentation of the results are available. In this work, we demonstrate the features of the software and show new methodological concepts and results produced by PyEMMA.