LangevinIntegrator

class simtk.openmm.openmm.LangevinIntegrator(*args)

This is an Integrator which simulates a System using Langevin dynamics.

__init__(self, temperature, frictionCoeff, stepSize) → LangevinIntegrator

__init__(self, other) -> LangevinIntegrator

Create a LangevinIntegrator.

Parameters:

• temperature (double) – the temperature of the heat bath (in Kelvin)
• frictionCoeff (double) – the friction coefficient which couples the system to the heat bath (in inverse picoseconds)
• stepSize (double) – the step size with which to integrate the system (in picoseconds)

Methods

__init__((self, temperature, frictionCoeff, ...)	__init__(self, other) -> LangevinIntegrator
getConstraintTolerance((self) -> double)	Get the distance tolerance within which constraints are maintained, as a fraction of the constrained distance.
getFriction((self) -> double)	Get the friction coefficient which determines how strongly the system is coupled to the heat bath (in inverse ps).
getRandomNumberSeed((self) -> int)	Get the random number seed.
getStepSize((self) -> double)	Get the size of each time step, in picoseconds.
getTemperature((self) -> double)	Get the temperature of the heat bath (in Kelvin).
setConstraintTolerance(self, tol)	Set the distance tolerance within which constraints are maintained, as a fraction of the constrained distance.
setFriction(self, coeff)	Set the friction coefficient which determines how strongly the system is coupled to the heat bath (in inverse ps).
setRandomNumberSeed(self, seed)	Set the random number seed.
setStepSize(self, size)	Set the size of each time step, in picoseconds.
setTemperature(self, temp)	Set the temperature of the heat bath (in Kelvin).
step(self, steps)	Advance a simulation through time by taking a series of time steps.

__copy__(self) → Integrator

getConstraintTolerance(self) → double

Get the distance tolerance within which constraints are maintained, as a fraction of the constrained distance.

getStepSize(self) → double

Get the size of each time step, in picoseconds. If this integrator uses variable time steps, the size of the most recent step is returned.

Returns:the step size, measured in ps Return type:double

setConstraintTolerance(self, tol)

Set the distance tolerance within which constraints are maintained, as a fraction of the constrained distance.

setStepSize(self, size)

Set the size of each time step, in picoseconds. If this integrator uses variable time steps, the effect of calling this method is undefined, and it may simply be ignored.

Parameters:size (double) – the step size, measured in ps

getTemperature(self) → double

Get the temperature of the heat bath (in Kelvin).

Returns:the temperature of the heat bath, measured in Kelvin Return type:double

setTemperature(self, temp)

Set the temperature of the heat bath (in Kelvin).

Parameters:temp (double) – the temperature of the heat bath, measured in Kelvin

getFriction(self) → double

Get the friction coefficient which determines how strongly the system is coupled to the heat bath (in inverse ps).

Returns:the friction coefficient, measured in 1/ps Return type:double

setFriction(self, coeff)

Set the friction coefficient which determines how strongly the system is coupled to the heat bath (in inverse ps).

Parameters:coeff (double) – the friction coefficient, measured in 1/ps

getRandomNumberSeed(self) → int

Get the random number seed. See setRandomNumberSeed() for details.

setRandomNumberSeed(self, seed)

Set the random number seed. The precise meaning of this parameter is undefined, and is left up to each Platform to interpret in an appropriate way. It is guaranteed that if two simulations are run with different random number seeds, the sequence of random forces will be different. On the other hand, no guarantees are made about the behavior of simulations that use the same seed. In particular, Platforms are permitted to use non-deterministic algorithms which produce different results on successive runs, even if those runs were initialized identically.

If seed is set to 0 (which is the default value assigned), a unique seed is chosen when a Context is created from this Force. This is done to ensure that each Context receives unique random seeds without you needing to set them explicitly.

step(self, steps)

Advance a simulation through time by taking a series of time steps.

Parameters:steps (int) – the number of time steps to take