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Rare event sampling
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<h2 id="time-dependence">Time dependence</h2> <p>If a system is out of <a href="/facts/Thermodynamic_equilibrium/1D7nGm0d">thermodynamic equilibrium</a>, then it is possible that there will be time-dependence in the rare event flux. In order to follow the time evolution of the probability of a rare event, it is necessary to maintain a steady current of trajectories into the target region of configurational space. SPRES is specifically designed for this eventuality and AMS is also at least formally valid for applications in which this is required. </p><p>In cases where a <a href="/facts/Dissipative/C134STy1">dissipative</a> steady state obtains (i.e. the conditions for thermodynamic equilibrium are not met, but the rare event flux is nonetheless constant) then FFS and other methods can be appropriate as well as the typically more expensive full-nonequilibrium approaches. </p> <h2 id="landscape-methods">Landscape methods</h2> <p>If the assumption of <a href="/facts/Thermodynamic_equilibrium/1D7nGm0d">thermodynamic equilibrium</a> is made, then there is no time-dependence in the rare event flux and a thermodynamic rather than statistical approach to the problem may be more appropriate. These methods are generally thought of separately to rare event methods, but may address the same problems. In these strategies, a free energy landscape (or an <a href="/facts/Energy_landscape/BRlfqt13">energy landscape</a>, for small systems) is prepared. For a small system this landscape may be mapped entirely, while for a system with a larger number of <a href="/facts/Degrees_of_freedom/D9RKXgml">degrees of freedom</a> a projection onto some set of progress coordinates will still be required. </p><p>Having mapped the landscape, and making certain assumptions, <a href="/facts/Transition-state_theory/s3MPBbpx">transition-state theory</a> can then be used to yield a description of the probabilities of paths within it. An example method for mapping landscapes is <a href="/facts/Replica_exchange/REv1b1yF">replica exchange</a> simulation, which has the advantage when applied to rare event problems that piecewise correct trajectory fragments are generated in the course of the method, allowing some direct analysis of the dynamic behaviour even without generating the full landscape. </p> <h2 id="see-also">See also</h2> <ul><li><a href="/facts/Rare_events/b8CfNSnh">Rare events</a></li></ul> <h2 id="related-software">Related software</h2> <ul><li><a href="/facts/R_package/etUNSDLc">R package</a> mistral (<a href="https://cran.r-project.org/package=mistral">CRAN</a> and <a href="https://github.com/ClementWalter/mistral">dev version</a>) for rare event simulation tools</li> <li>The <a href="/facts/Python_(programming_language)/YbuGqofa">Python</a> toolset <a href="http://www.freshs.org">freshs.org</a> as an example toolkit for distributing FFS and SPRES calculations to run sampling trials concurrently on parallel hardware or in a distributed manner across the network.</li> <li><a href="http://www.pyretis.org/">Pyretis</a>,<a class="footnote-ref" id="fnref:16" href="#fn:16"><sup>16</sup></a> an opensource python library to perform TIS (and RETIS) simulations. It is interfaced with common software for MD <a href="/facts/GROMACS/k9uuIs0e">GROMACS</a> and QM/MD <a href="/facts/CP2K/g84Xktgq">CP2K</a> simulations.</li> <li><a href="https://westpa.github.io/westpa/">WESTPA</a> and <a href="https://github.com/ADicksonLab/wepy">wepy</a> are packages for Weighted Ensemble.</li> <li><a href="http://www.pyretis.org/">PyVisA</a>, <a class="footnote-ref" id="fnref:17" href="#fn:17"><sup>17</sup></a> An analysis and Visualization software for path sampling outputs with the integration of machine learning based algorithms.</li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>Morio, J.; Balesdent, M. (2014). "A survey of rare event simulation methods for static input–output models" (PDF). Simulation Modelling Practice and Theory. 49 (4): 287–304. doi:10.1016/j.simpat.2014.10.007. <a href="https://hal.archives-ouvertes.fr/hal-01081888/file/reviewrareevent_v2.pdf" target="_blank">https://hal.archives-ouvertes.fr/hal-01081888/file/reviewrareevent_v2.pdf</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Dellago, Christoph; Bolhuis, Peter G.; Geissler, Phillip L. (2002). Transition Path Sampling. Vol. 123. pp. 1–84. doi:10.1002/0471231509.ch1. ISBN 978-0-471-21453-3. {{cite book}}: |journal= ignored (help) <a href="978-0-471-21453-3" target="_blank">978-0-471-21453-3</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>Riccardi, Enrico; Dahlen, Oda; van Erp, Titus S. (2017-09-06). "Fast Decorrelating Monte Carlo Moves for Efficient Path Sampling". The Journal of Physical Chemistry Letters. 8 (18): 4456–4460. doi:10.1021/acs.jpclett.7b01617. hdl:11250/2491276. ISSN 1948-7185. 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