SimulatedTempering

class openmm.app.simulatedtempering.SimulatedTempering(simulation, temperatures=None, numTemperatures=None, minTemperature=None, maxTemperature=None, weights=None, tempChangeInterval=25, reportInterval=1000, reportFile=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>)

SimulatedTempering implements the simulated tempering algorithm for accelerated sampling.

It runs a simulation while allowing the temperature to vary. At high temperatures, it can more easily cross energy barriers to explore a wider area of conformation space. At low temperatures, it can thoroughly explore each local region. For details, see Marinari, E. and Parisi, G., Europhys. Lett. 19(6). pp. 451-458 (1992).

The set of temperatures to sample can be specified in two ways. First, you can explicitly provide a list of temperatures by using the “temperatures” argument. Alternatively, you can specify the minimum and maximum temperatures, and the total number of temperatures to use. The temperatures are chosen spaced exponentially between the two extremes. For example,

st = SimulatedTempering(simulation, numTemperatures=15, minTemperature=300*kelvin, maxTemperature=450*kelvin)

After creating the SimulatedTempering object, call step() on it to run the simulation.

Transitions between temperatures are performed at regular intervals, as specified by the “tempChangeInterval” argument. For each transition, a new temperature is selected using the independence sampling method, as described in Chodera, J. and Shirts, M., J. Chem. Phys. 135, 194110 (2011).

Simulated tempering requires a “weight factor” for each temperature. Ideally, these should be chosen so the simulation spends equal time at every temperature. You can specify the list of weights to use with the optional “weights” argument. If this is omitted, weights are selected automatically using the Wang-Landau algorithm as described in Wang, F. and Landau, D. P., Phys. Rev. Lett. 86(10), pp. 2050-2053 (2001).

To properly analyze the results of the simulation, it is important to know the temperature and weight factors at every point in time. The SimulatedTempering object functions as a reporter, writing this information to a file or stdout at regular intervals (which should match the interval at which you save frames from the simulation). You can specify the output file and reporting interval with the “reportFile” and “reportInterval” arguments.

__init__(simulation, temperatures=None, numTemperatures=None, minTemperature=None, maxTemperature=None, weights=None, tempChangeInterval=25, reportInterval=1000, reportFile=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>)

Create a new SimulatedTempering.

Parameters

  • simulation (Simulation) – The Simulation defining the System, Context, and Integrator to use

  • temperatures (list) – The list of temperatures to use for tempering, in increasing order

  • numTemperatures (int) – The number of temperatures to use for tempering. If temperatures is not None, this is ignored.

  • minTemperature (temperature) – The minimum temperature to use for tempering. If temperatures is not None, this is ignored.

  • maxTemperature (temperature) – The maximum temperature to use for tempering. If temperatures is not None, this is ignored.

  • weights (list) – The weight factor for each temperature. If none, weights are selected automatically.

  • tempChangeInterval (int) – The interval (in time steps) at which to attempt transitions between temperatures

  • reportInterval (int) – The interval (in time steps) at which to write information to the report file

  • reportFile (string or file) – The file to write reporting information to, specified as a file name or file object

Methods

__init__(simulation[, temperatures, …])

Create a new SimulatedTempering.

step(steps)

Advance the simulation by integrating a specified number of time steps.

Attributes

weights

step(steps)

Advance the simulation by integrating a specified number of time steps.