# md.methods

Overview

 Method Base class integration method. Brownian Brownian dynamics. Langevin Langevin dynamics. NPH NPH Integration via MTK barostat-thermostat. NPT NPT Integration via MTK barostat-thermostat. NVE NVE Integration via Velocity-Verlet. NVT NVT Integration via the Nosé-Hoover thermostat. OverdampedViscous Overdamped viscous dynamics.

Details

Integration methods for molecular dynamics.

For methods that constrain motion to a manifold see hoomd.md.methods.rattle

class hoomd.md.methods.Brownian(filter, kT, alpha=None)

Brownian dynamics.

Parameters

Brownian integrates particles forward in time according to the overdamped Langevin equations of motion, sometimes called Brownian dynamics, or the diffusive limit.

\begin{align}\begin{aligned}\frac{d\vec{x}}{dt} = \frac{\vec{F}_\mathrm{C} + \vec{F}_\mathrm{R}}{\gamma}\\\langle \vec{F}_\mathrm{R} \rangle = 0\\\langle |\vec{F}_\mathrm{R}|^2 \rangle = 2 d k T \gamma / \delta t\\\langle \vec{v}(t) \rangle = 0\\\langle |\vec{v}(t)|^2 \rangle = d k T / m\end{aligned}\end{align}

where $$\vec{F}_\mathrm{C}$$ is the force on the particle from all potentials and constraint forces, $$\gamma$$ is the drag coefficient, $$\vec{F}_\mathrm{R}$$ is a uniform random force, $$\vec{v}$$ is the particle’s velocity, and $$d$$ is the dimensionality of the system. The magnitude of the random force is chosen via the fluctuation-dissipation theorem to be consistent with the specified drag and temperature, $$T$$. When $$kT=0$$, the random force $$\vec{F}_\mathrm{R}=0$$.

Brownian uses the integrator from I. Snook, The Langevin and Generalised Langevin Approach to the Dynamics of Atomic, Polymeric and Colloidal Systems, 2007, section 6.2.5 link, with the exception that $$\vec{F}_\mathrm{R}$$ is drawn from a uniform random number distribution.

In Brownian dynamics, particle velocities are completely decoupled from positions. At each time step, Brownian draws a new velocity distribution consistent with the current set temperature so that hoomd.md.compute.ThermodynamicQuantities will report appropriate temperatures and pressures when logged or used by other methods.

Brownian dynamics neglects the acceleration term in the Langevin equation. This assumption is valid when overdamped: $$\frac{m}{\gamma} \ll \delta t$$. Use Langevin if your system is not overdamped.

You can specify $$\gamma$$ in two ways:

1. Specify $$\alpha$$ which scales the particle diameter to $$\gamma = \alpha d_i$$. The units of $$\alpha$$ are mass / distance / time.

2. After the method object is created, specify the attribute gamma and gamma_r for rotational damping or random torque to assign them directly, with independent values for each particle type in the system.

Examples:

brownian = hoomd.md.methods.Brownian(filter=hoomd.filter.All(), kT=0.2,
alpha=1.0)
integrator = hoomd.md.Integrator(dt=0.001, methods=[brownian],
forces=[lj])


Examples of using gamma pr gamma_r on drag coefficient:

brownian = hoomd.md.methods.Brownian(filter=hoomd.filter.All(), kT=0.2)
brownian.gamma.default = 2.0
brownian.gamma_r.default = [1.0, 2.0, 3.0]

filter

Subset of particles to apply this method to.

Type

hoomd.filter.ParticleFilter

kT

Temperature of the simulation $$[\mathrm{energy}]$$.

Type

hoomd.variant.Variant

alpha

When set, use $$\alpha d_i$$ for the drag coefficient where $$d_i$$ is particle diameter $$[\mathrm{mass} \cdot \mathrm{length}^{-1} \cdot \mathrm{time}^{-1}]$$. Defaults to None.

Type

float

gamma

The drag coefficient can be directly set instead of the ratio of particle diameter ($$\gamma = \alpha d_i$$). The type of gamma parameter is either positive float or zero $$[\mathrm{mass} \cdot \mathrm{time}^{-1}]$$.

Type

TypeParameter[ particle type, float ]

gamma_r

The rotational drag coefficient can be set. The type of gamma_r parameter is a tuple of three float. The type of each element of tuple is either positive float or zero $$[\mathrm{force} \cdot \mathrm{length} \cdot \mathrm{radian}^{-1} \cdot \mathrm{time}^{-1}]$$.

Type

TypeParameter[particle type, [float, float, float]]

class hoomd.md.methods.Langevin(filter, kT, alpha=None, tally_reservoir_energy=False)

Langevin dynamics.

Parameters
• filter (hoomd.filter.ParticleFilter) – Subset of particles to apply this method to.

• kT (hoomd.variant.Variant or float) – Temperature of the simulation $$[\mathrm{energy}]$$.

• alpha (float) – When set, use $$\alpha d_i$$ for the drag coefficient where $$d_i$$ is particle diameter $$[\mathrm{mass} \cdot \mathrm{length}^{-1} \cdot \mathrm{time}^{-1}]$$. Defaults to None.

• tally_reservoir_energy (bool) – If true, the energy exchange between the thermal reservoir and the particles is tracked. Total energy conservation can then be monitored by adding langevin_reservoir_energy_groupname to the logged quantities. Defaults to False $$[\mathrm{energy}]$$.

Translational degrees of freedom

Langevin integrates particles forward in time according to the Langevin equations of motion:

\begin{align}\begin{aligned}m \frac{d\vec{v}}{dt} = \vec{F}_\mathrm{C} - \gamma \cdot \vec{v} + \vec{F}_\mathrm{R}\\\langle \vec{F}_\mathrm{R} \rangle = 0\\\langle |\vec{F}_\mathrm{R}|^2 \rangle = 2 d kT \gamma / \delta t\end{aligned}\end{align}

where $$\vec{F}_\mathrm{C}$$ is the force on the particle from all potentials and constraint forces, $$\gamma$$ is the drag coefficient, $$\vec{v}$$ is the particle’s velocity, $$\vec{F}_\mathrm{R}$$ is a uniform random force, and $$d$$ is the dimensionality of the system (2 or 3). The magnitude of the random force is chosen via the fluctuation-dissipation theorem to be consistent with the specified drag and temperature, $$T$$. When $$kT=0$$, the random force $$\vec{F}_\mathrm{R}=0$$.

Langevin dynamics includes the acceleration term in the Langevin equation and is useful for gently thermalizing systems using a small gamma. This assumption is valid when underdamped: $$\frac{m}{\gamma} \gg \delta t$$. Use Brownian if your system is not underdamped.

Langevin uses the same integrator as NVE with the additional force term $$- \gamma \cdot \vec{v} + \vec{F}_\mathrm{R}$$. The random force $$\vec{F}_\mathrm{R}$$ is drawn from a uniform random number distribution.

You can specify $$\gamma$$ in two ways:

1. Specify $$\alpha$$ which scales the particle diameter to $$\gamma = \alpha d_i$$. The units of $$\alpha$$ are mass / distance / time.

2. After the method object is created, specify the attribute gamma and gamma_r for rotational damping or random torque to assign them directly, with independent values for each particle type in the system.

Warning

When restarting a simulation, the energy of the reservoir will be reset to zero.

Examples:

langevin = hoomd.md.methods.Langevin(filter=hoomd.filter.All(), kT=0.2,
alpha=1.0)
integrator = hoomd.md.Integrator(dt=0.001, methods=[langevin],
forces=[lj])


Examples of using gamma or gamma_r on drag coefficient:

langevin = hoomd.md.methods.Langevin(filter=hoomd.filter.All(), kT=0.2)
langevin.gamma.default = 2.0
langevin.gamma_r.default = [1.0,2.0,3.0]

filter

Subset of particles to apply this method to.

Type

hoomd.filter.ParticleFilter

kT

Temperature of the simulation $$[\mathrm{energy}]$$.

Type

hoomd.variant.Variant

alpha

When set, use $$\alpha d_i$$ for the drag coefficient where $$d_i$$ is particle diameter $$[\mathrm{mass} \cdot \mathrm{length}^{-1} \cdot \mathrm{time}^{-1}]$$. Defaults to None.

Type

float

gamma

The drag coefficient can be directly set instead of the ratio of particle diameter ($$\gamma = \alpha d_i$$). The type of gamma parameter is either positive float or zero $$[\mathrm{mass} \cdot \mathrm{time}^{-1}]$$.

Type

TypeParameter[ particle type, float ]

gamma_r

The rotational drag coefficient can be set. The type of gamma_r parameter is a tuple of three float. The type of each element of tuple is either positive float or zero $$[\mathrm{mass} \cdot \mathrm{time}^{-1}]$$.

Type

TypeParameter[particle type,[float, float , float]]

class hoomd.md.methods.Method

Base class integration method.

Provides common methods for all subclasses.

Note

Users should use the subclasses and not instantiate Method directly.

class hoomd.md.methods.NPH(filter, S, tauS, couple, box_dof=(True, True, True, False, False, False), rescale_all=False, gamma=0.0)

NPH Integration via MTK barostat-thermostat.

Parameters
• filter (hoomd.filter.ParticleFilter) – Subset of particles on which to apply this method.

• S – Stress components set point for the barostat. In Voigt notation: $$[S_{xx}, S_{yy}, S_{zz}, S_{yz}, S_{xz}, S_{xy}]$$ $$[\mathrm{pressure}]$$. In case of isotropic pressure P ($$[p, p, p, 0, 0, 0]$$), use S = p. Accepts: tuple [ hoomd.variant.Variant or float, … ] or hoomd.variant.Variant or float.

• tauS (float) – Coupling constant for the barostat $$[\mathrm{time}]$$.

• couple (str) – Couplings of diagonal elements of the stress tensor, can be “none”, “xy”, “xz”,”yz”, or “all”, default to “all”.

• box_dof (tuple [ bool ]) – Box degrees of freedom with six boolean elements corresponding to x, y, z, xy, xz, yz, each. Default to [True,True,True,False,False,False]). If turned on to True, rescale corresponding lengths or tilt factors and components of particle coordinates and velocities.

• rescale_all (bool) – if True, rescale all particles, not only those in the group, Default to False.

• gamma (float) – Dimensionless damping factor for the box degrees of freedom, Default to 0.

Note

Coupling constant for barostat tauS should be set within appropriate range for pressure and volume to fluctuate in reasonable rate and equilibrate. Too small tauS can cause abrupt fluctuation, whereas too large tauS would take long time to equilibrate. In most of systems, recommended value for tauS is 1000 * dt, where dt is the length of the time step.

Examples:

dt = 0.005
tauS = 1000 * dt
nph = hoomd.md.methods.NPH(filter=hoomd.filter.All(), tauS=tauS, S=2.0)
# orthorhombic symmetry
nph = hoomd.md.methods.NPH(filter=hoomd.filter.All(), tauS=tauS, S=2.0,
couple="none")
# tetragonal symmetry
nph = hoomd.md.methods.NPH(filter=hoomd.filter.All(), tauS=tauS, S=2.0,
couple="xy")
# triclinic symmetry
nph = hoomd.md.methods.NPH(filter=hoomd.filter.All(), tauS=tauS, S=2.0,
couple="none", rescale_all=True)
integrator = hoomd.md.Integrator(dt=dt, methods=[nph], forces=[lj])

filter

Subset of particles on which to apply this method.

Type

hoomd.filter.ParticleFilter

S

Stress components set point for the barostat totalling 6 components. In Voigt notation, $$[S_{xx}, S_{yy}, S_{zz}, S_{yz}, S_{xz}, S_{xy}]$$ $$[\mathrm{pressure}]$$. Stress can be reset after method object is created. For example, an isoropic pressure can be set by nph.S = 4.

Type
tauS

Coupling constant for the barostat $$[\mathrm{time}]$$.

Type

float

couple

Couplings of diagonal elements of the stress tensor, can be “none”, “xy”, “xz”,”yz”, or “all”.

Type

str

box_dof

Box degrees of freedom with six boolean elements corresponding to x, y, z, xy, xz, yz, each.

Type
rescale_all

if True, rescale all particles, not only those in the group.

Type

bool

gamma

Dimensionless damping factor for the box degrees of freedom.

Type

float

barostat_dof

Additional degrees of freedom for the barostat ($$\nu_{xx}$$, $$\nu_{xy}$$, $$\nu_{xz}$$, $$\nu_{yy}$$, $$\nu_{yz}$$, $$\nu_{zz}$$)

Type
property barostat_energy

Energy the barostat contributes to the Hamiltonian $$[\mathrm{energy}]$$.

(Loggable: category=”scalar”)

thermalize_barostat_dof()

Set the barostat momentum to random values.

thermalize_barostat_dof sets a random value for the barostat $$\nu_{\mathrm{ij}}$$. Call thermalize_barostat_dof to set a new random state for the barostat.

Important

You must call Simulation.run before thermalize_barostat_dof. Call run(steps=0) to prepare a newly created hoomd.Simulation.

class hoomd.md.methods.NPT(filter, kT, tau, S, tauS, couple, box_dof=[True, True, True, False, False, False], rescale_all=False, gamma=0.0)

NPT Integration via MTK barostat-thermostat.

Parameters

NPT performs constant pressure, constant temperature simulations, allowing for a fully deformable simulation box.

The integration method is based on the rigorous Martyna-Tobias-Klein equations of motion for NPT. For optimal stability, the update equations leave the phase-space measure invariant and are manifestly time-reversible.

By default, NPT performs integration in a cubic box under hydrostatic pressure by simultaneously rescaling the lengths Lx, Ly and Lz of the simulation box.

NPT can also perform more advanced integration modes. The integration mode is specified by a set of couplings and by specifying the box degrees of freedom that are put under barostat control.

Couplings define which diagonal elements of the pressure tensor $$P_{\alpha,\beta}$$ should be averaged over, so that the corresponding box lengths are rescaled by the same amount.

Valid couplings are:

• none (all box lengths are updated independently)

• xy (Lx and Ly are coupled)

• xz (Lx and Lz are coupled)

• yz (Ly and Lz are coupled)

• xyz (Lx, Ly, and Lz are coupled)

Degrees of freedom of the box specify which lengths and tilt factors of the box should be updated, and how particle coordinates and velocities should be rescaled. The box_dof tuple controls the way the box is rescaled and updated. The first three elements box_dof[:3] controls whether the x, y, and z box lengths are rescaled and updated, respectively. The last three entries box_dof[3:] control the rescaling or the tilt factors xy, xz, and yz. All options also appropriately rescale particle coordinates and velocities.

By default, the x, y, and z degrees of freedom are updated. [True,True,True,False,False,False]

Note

If any of the diagonal x, y, z degrees of freedom is not being integrated, pressure tensor components along that direction are not considered for the remaining degrees of freedom.

For example:

• Specifying all couplings and x, y, and z degrees of freedom amounts to cubic symmetry (default)

• Specifying xy couplings and x, y, and z degrees of freedom amounts to tetragonal symmetry.

• Specifying no couplings and all degrees of freedom amounts to a fully deformable triclinic unit cell

For the MTK equations of motion, see:

$$\tau$$ is related to the Nosé mass $$Q$$ by

$\tau = \sqrt{\frac{Q}{g k T_0}}$

where $$g$$ is the number of degrees of freedom, and $$k T_0$$ is the set point (kT above).

The NPT equations of motion include a translational thermostat (with momentum $$\xi$$ and position $$\eta$$), a rotational thermostat (with momentum $$\xi_{\mathrm{rot}}$$ and position $$\eta_\mathrm{rot}$$), and a barostat tensor $$\nu_{\mathrm{ij}}$$. Access these quantities using translational_thermostat_dof, rotational_thermostat_dof, and barostat_dof.

Note

Coupling constant for barostat tauS should be set within appropriate range for pressure and volume to fluctuate in reasonable rate and equilibrate. Too small tauS can cause abrupt fluctuation, whereas too large tauS would take long time to equilibrate. In most of systems, recommended value for tauS is 1000 * dt, where dt is the length of the time step.

Examples:

npt = hoomd.md.methods.NPT(filter=hoomd.filter.All(), tau=1.0, kT=0.65,
tauS = 1.2, S=2.0, couple="xyz")
# orthorhombic symmetry
npt = hoomd.md.methods.NPT(filter=hoomd.filter.All(), tau=1.0, kT=0.65,
tauS = 1.2, S=2.0, couple="none")
# tetragonal symmetry
npt = hoomd.md.methods.NPT(filter=hoomd.filter.All(), tau=1.0, kT=0.65,
tauS = 1.2, S=2.0, couple="xy")
# triclinic symmetry
npt = hoomd.md.methods.NPT(filter=hoomd.filter.All(), tau=1.0, kT=0.65,
tauS = 1.2, S=2.0, couple="none", rescale_all=True)
integrator = hoomd.md.Integrator(dt=0.005, methods=[npt], forces=[lj])

filter

Subset of particles on which to apply this method.

Type

hoomd.filter.ParticleFilter

kT

Temperature set point for the thermostat $$[\mathrm{energy}]$$.

Type

hoomd.variant.Variant

tau

Coupling constant for the thermostat $$[\mathrm{time}]$$.

Type

float

S

Stress components set point for the barostat. In Voigt notation, $$[S_{xx}, S_{yy}, S_{zz}, S_{yz}, S_{xz}, S_{xy}]$$ $$[\mathrm{pressure}]$$. Stress can be reset after the method object is created. For example, an isotropic pressure can be set by npt.S = 4.

Type
tauS

Coupling constant for the barostat $$[\mathrm{time}]$$.

Type

float

couple

Couplings of diagonal elements of the stress tensor, can be “none”, “xy”, “xz”,”yz”, or “xyz”.

Type

str

box_dof

Box degrees of freedom with six boolean elements corresponding to x, y, z, xy, xz, yz, each.

Type

list[bool]

rescale_all

if True, rescale all particles, not only those in the group.

Type

bool

gamma

Dimensionless damping factor for the box degrees of freedom.

Type

float

translational_thermostat_dof

Additional degrees of freedom for the translational thermostat ($$\xi$$, $$\eta$$)

Type
rotational_thermostat_dof

Additional degrees of freedom for the rotational thermostat ($$\xi_\mathrm{rot}$$, $$\eta_\mathrm{rot}$$)

Type
barostat_dof

Additional degrees of freedom for the barostat ($$\nu_{xx}$$, $$\nu_{xy}$$, $$\nu_{xz}$$, $$\nu_{yy}$$, $$\nu_{yz}$$, $$\nu_{zz}$$)

Type
property barostat_energy

Energy the barostat contributes to the Hamiltonian $$[\mathrm{energy}]$$.

(Loggable: category=”scalar”)

thermalize_thermostat_and_barostat_dof()

Set the thermostat and barostat momenta to random values.

thermalize_thermostat_and_barostat_dof sets a random value for the momentum $$\xi$$ and the barostat $$\nu_{\mathrm{ij}}$$. When Integrator.integrate_rotational_dof is True, it also sets a random value for the rotational thermostat momentum $$\xi_{\mathrm{rot}}$$. Call thermalize_thermostat_and_barostat_dof to set a new random state for the thermostat and barostat.

Important

You must call Simulation.run before thermalize_thermostat_and_barostat_dof. Call run(steps=0) to prepare a newly created hoomd.Simulation.

property thermostat_energy

Energy the thermostat contributes to the Hamiltonian $$[\mathrm{energy}]$$.

(Loggable: category=”scalar”)

class hoomd.md.methods.NVE(filter, limit=None)

NVE Integration via Velocity-Verlet.

Parameters

NVE performs constant volume, constant energy simulations using the standard Velocity-Verlet method. For poor initial conditions that include overlapping atoms, a limit can be specified to the movement a particle is allowed to make in one time step. After a few thousand time steps with the limit set, the system should be in a safe state to continue with unconstrained integration.

Examples:

nve = hoomd.md.methods.NVE(filter=hoomd.filter.All())
integrator = hoomd.md.Integrator(dt=0.005, methods=[nve], forces=[lj])

filter

Subset of particles on which to apply this method.

Type

hoomd.filter.ParticleFilter

limit

Enforce that no particle moves more than a distance of a limit in a single time step. Defaults to None

Type

None or float

class hoomd.md.methods.NVT(filter, kT, tau)

NVT Integration via the Nosé-Hoover thermostat.

Parameters

NVT performs constant volume, constant temperature simulations using the Nosé-Hoover thermostat, using the MTK equations described in Refs. G. J. Martyna, D. J. Tobias, M. L. Klein 1994 and J. Cao, G. J. Martyna 1996.

$$\tau$$ is related to the Nosé mass $$Q$$ by

$\tau = \sqrt{\frac{Q}{g k T_0}}$

where $$g$$ is the number of degrees of freedom, and $$k T_0$$ is the set point (kT above).

The NVT equations of motion include a translational thermostat (with momentum $$\xi$$ and position $$\eta$$) and a rotational thermostat (with momentum $$\xi_{\mathrm{rot}}$$ and position $$\eta_\mathrm{rot}$$). Access these quantities using translational_thermostat_dof and rotational_thermostat_dof.

Note

Coupling constant tau in Nosé-Hoover thermostat should be set within reasonable range to avoid abrupt fluctuation in temperature in case of small tau , also to avoid long time to equilibrate in case of large tau. Recommended value for most of systems is 100 * dt, where dt is the length of the time step.

Examples:

nvt=hoomd.md.methods.NVT(filter=hoomd.filter.All(), kT=1.0, tau=0.5)
integrator = hoomd.md.Integrator(dt=0.005, methods=[nvt], forces=[lj])

filter

Subset of particles on which to apply this method.

Type

hoomd.filter.ParticleFilter

kT

Temperature set point for the Nosé-Hoover thermostat $$[\mathrm{energy}]$$.

Type

hoomd.variant.Variant

tau

Coupling constant for the Nosé-Hoover thermostat $$[\mathrm{time}]$$.

Type

float

translational_thermostat_dof

Additional degrees of freedom for the translational thermostat ($$\xi$$, $$\eta$$)

Type
rotational_thermostat_dof

Additional degrees of freedom for the rotational thermostat ($$\xi_\mathrm{rot}$$, $$\eta_\mathrm{rot}$$)

Type
thermalize_thermostat_dof()

Set the thermostat momenta to random values.

thermalize_thermostat_dof sets a random value for the momentum $$\xi$$. When Integrator.integrate_rotational_dof is True, it also sets a random value for the rotational thermostat momentum $$\xi_{\mathrm{rot}}$$. Call thermalize_thermostat_dof to set a new random state for the thermostat.

Important

You must call Simulation.run before thermalize_thermostat_dof. Call run(steps=0) to prepare a newly created hoomd.Simulation.

property thermostat_energy

Energy the thermostat contributes to the Hamiltonian $$[\mathrm{energy}]$$.

(Loggable: category=”scalar”)

class hoomd.md.methods.OverdampedViscous(filter, alpha=None)

Overdamped viscous dynamics.

Parameters
• filter (hoomd.filter.ParticleFilter) – Subset of particles to apply this method to.

• alpha (float) – When set, use $$\alpha d_i$$ for the drag coefficient where $$d_i$$ is particle diameter. Defaults to None $$[\mathrm{mass} \cdot \mathrm{length}^{-1} \cdot \mathrm{time}^{-1}]$$.

OverdampedViscous integrates particles forward in time following Newtonian in the overdamped limit where there is no intertial term. (in the limit that the mass $$m$$ goes to 0).

\begin{align}\begin{aligned}\frac{d\vec{r}}{dt} = \vec{v}\\\vec{v(t)} = \frac{\vec{F}_\mathrm{C}}{\gamma}\end{aligned}\end{align}

where $$\vec{F}_\mathrm{C}$$ is the force on the particle from all potentials, $$\gamma$$ is the drag coefficient, $$\vec{v}$$ is the particle’s velocity, and $$d$$ is the dimensionality of the system.

You can specify $$\gamma$$ in two ways:

1. Specify $$\alpha$$ which scales the particle diameter to $$\gamma = \alpha d_i$$. The units of $$\alpha$$ are $$[\mathrm{mass} \cdot \mathrm{length}^{-1} \cdot \mathrm{time}^{-1}]$$.

2. After the method object is created, specify the attribute gamma and gamma_r for rotational damping or random torque to assign them directly, with independent values for each particle type in the system.

Tip

OverdampedViscous can be used to simulate systems of athermal active matter, such as athermal Active Brownian Particles.

Examples:

odv = hoomd.md.methods.Brownian(filter=hoomd.filter.All())
odv.gamma.default = 2.0
odv.gamma_r.default = [1.0, 2.0, 3.0]

filter

Subset of particles to apply this method to.

Type

hoomd.filter.ParticleFilter

alpha

When set, use $$\alpha d_i$$ for the drag coefficient where $$d_i$$ is particle diameter $$[\mathrm{mass} \cdot \mathrm{length}^{-1} \cdot \mathrm{time}^{-1}]$$. Defaults to None.

Type

float

gamma

The drag coefficient can be directly set instead of the ratio of particle diameter ($$\gamma = \alpha d_i$$). The type of gamma parameter is either positive float or zero $$[\mathrm{mass} \cdot \mathrm{time}^{-1}]$$.

Type

TypeParameter[ particle type, float ]

gamma_r

The rotational drag coefficient can be set. The type of gamma_r parameter is a tuple of three float. The type of each element of tuple is either positive float or zero $$[\mathrm{force} \cdot \mathrm{length} \cdot \mathrm{radian}^{-1} \cdot \mathrm{time}^{-1}]$$.

Type

TypeParameter[particle type, [float, float, float]]

Modules