19. Standard Forces

The following classes implement standard force field terms that are widely used in molecular simulations.

19.1. HarmonicBondForce

Each harmonic bond is represented by an energy term of the form

\[E=\frac{1}{2}k{\left(x-{x}_{0}\right)}^{2}\]

where x is the distance between the two particles, x₀ is the equilibrium distance, and k is the force constant. This produces a force of magnitude k(x-x₀).

Be aware that some force fields define their harmonic bond parameters in a slightly different way: E = k´(x-x₀)², leading to a force of magnitude 2k´(x-x₀). Comparing these two forms, you can see that k = 2k´. Be sure to check which form a particular force field uses, and if necessary multiply the force constant by 2.

19.2. HarmonicAngleForce

Each harmonic angle is represented by an energy term of the form

\[E=\frac{1}{2}k{\left(\theta-\theta_0\right)}^{2}\]

where \(\theta\) is the angle formed by the three particles, \(\theta_0\) is the equilibrium angle, and k is the force constant.

As with HarmonicBondForce, be aware that some force fields define their harmonic angle parameters as E = k´(\(\theta\)-\(\theta\)₀)². Be sure to check which form a particular force field uses, and if necessary multiply the force constant by 2.

19.3. PeriodicTorsionForce

Each torsion is represented by an energy term of the form

\[E=k\left(1+\text{cos}\left(n\theta-\theta_0\right)\right)\]

where \(\theta\) is the dihedral angle formed by the four particles, \(\theta_0\) is the phase offset, n is the periodicity, and k is the force constant.

19.4. RBTorsionForce

Each torsion is represented by an energy term of the form

\[E=\sum _{i=0}^{5}{C}_{i}{\left(\text{cos}\phi\right)}^{i}\]

where \(\phi\) is the dihedral angle formed by the four particles and C₀ through C₅ are constant coefficients.

For reason of convention, PeriodicTorsionForce and RBTorsonForce define the torsion angle differently. \(\theta\) is zero when the first and last particles are on the same side of the bond formed by the middle two particles (the cis configuration), whereas \(\phi\) is zero when they are on opposite sides (the trans configuration). This means that \(\theta\) = \(\phi\) - \(\pi\).

19.5. CMAPTorsionForce

Each torsion pair is represented by an energy term of the form

\[E=f\left(\theta_1,\theta_2\right)\]

where \(\theta_1\) and \(\theta_2\) are the two dihedral angles coupled by the term, and f(x,y) is defined by a user-supplied grid of tabulated values. A natural cubic spline surface is fit through the tabulated values, then evaluated to determine the energy for arbitrary (\(\theta_1\), \(\theta_2\)) pairs.

19.6. NonbondedForce

19.6.1. Lennard-Jones Interaction

The Lennard-Jones interaction between each pair of particles is represented by an energy term of the form

\[E=4\epsilon\left({\left(\frac{\sigma}{r}\right)}^{12}-{\left(\frac{\sigma}{r}\right)}^{6}\right)\]

where r is the distance between the two particles, \(\sigma\) is the distance at which the energy equals zero, and \(\epsilon\) sets the strength of the interaction. If the NonbondedMethod in use is anything other than NoCutoff and r is greater than the cutoff distance, the energy and force are both set to zero. Because the interaction decreases very quickly with distance, the cutoff usually has little effect on the accuracy of simulations.

Optionally you can use a switching function to make the energy go smoothly to 0 at the cutoff distance. When \(r_\mathit{switch} < r < r_\mathit{cutoff}\), the energy is multiplied by

\[S=1-{6x}^{5}+15{x}^{4}-10{x}^{3}\]

where \(x = (r-r_\mathit{switch})/(r_\mathit{cutoff}-r_\mathit{switch})\). This function decreases smoothly from 1 at \(r = r_\mathit{switch}\) to 0 at \(r = r_\mathit{cutoff}\), and has continuous first and second derivatives at both ends.

When an exception has been added for a pair of particles, \(\sigma\) and \(\epsilon\) are the parameters specified by the exception. Otherwise they are determined from the parameters of the individual particles using the Lorentz-Berthelot combining rule:

\[\sigma=\frac{\sigma_1+\sigma_2}{2}\]

\[\epsilon=\sqrt{\epsilon_1 \epsilon_2}\]

When using periodic boundary conditions, NonbondedForce can optionally add a term (known as a long range dispersion correction) to the energy that approximately represents the contribution from all interactions beyond the cutoff distance:[44]

\[{E}_{\text{cor}}=\frac{{8\pi N}^{2}}{V}\left(\frac{\langle \epsilon_{ij}\sigma_{ij}^{12}\rangle}{{9r_c}^9}-\frac{\langle \epsilon_{ij}\sigma_{ij}^{6}\rangle}{{3r_c}^3}\right)\]

where N is the number of particles in the system, V is the volume of the periodic box, \(r_c\) is the cutoff distance, \(\sigma_{ij}\) and \(\epsilon_{ij}\) are the interaction parameters between particle i and particle j, and \(\langle \text{...} \rangle\) represents an average over all pairs of particles in the system. When a switching function is in use, there is also a contribution to the correction that depends on the integral of E·(1-S) over the switching interval. The long range dispersion correction is primarily useful when running simulations at constant pressure, since it produces a more accurate variation in system energy with respect to volume.

The Lennard-Jones interaction is often parameterized in two other equivalent ways. One is

\[E=\epsilon\left({\left(\frac{{r}_{\mathit{min}}}{r}\right)}^{\text{12}}-2{\left(\frac{{r}_{\mathit{min}}}{r}\right)}^{6}\right)\]

where \(r_\mathit{min}\) (sometimes known as \(d_\mathit{min}\); this is not a radius) is the center-to-center distance at which the energy is minimum. It is related to \(\sigma\) by

\[\sigma=\frac{{r}_{\mathit{min}}}{{2}^{1/6}}\]

In turn, \(r_\mathit{min}\) is related to the van der Waals radius by \(r_\mathit{min} = 2r_\mathit{vdw}\).

Another common form is

\[E=\frac{A}{{r}^{\text{12}}}-\frac{B}{{r}^{6}}\]

The coefficients A and B are related to \(\sigma\) and \(\epsilon\) by

\[\sigma={\left(\frac{A}{B}\right)}^{1/6}\]

\[\epsilon=\frac{{B}^{2}}{4A}\]

19.6.2. Coulomb Interaction Without Cutoff

The form of the Coulomb interaction between each pair of particles depends on the NonbondedMethod in use. For NoCutoff, it is given by

\[E=\frac{1}{4{\pi}{\epsilon}_{0}}\frac{{q}_{1}{q}_{2}}{r}\]

where q₁ and q₂ are the charges of the two particles, and r is the distance between them.

19.6.3. Coulomb Interaction With Cutoff

For CutoffNonPeriodic or CutoffPeriodic, it is modified using the reaction field approximation. This is derived by assuming everything beyond the cutoff distance is a solvent with a uniform dielectric constant.[45]

\[E=\frac{{q}_{1}{q}_{2}}{4\pi\epsilon_0}\left(\frac{1}{r}+{k}_{\mathit{rf}}{r}^{2}-{c}_{\mathit{rf}}\right)\]

\[{k}_{\mathit{rf}}=\left(\frac{1}{{r_\mathit{cutoff}}^3}\right)\left(\frac{{\epsilon}_{\mathit{solvent}}-1}{2{\epsilon}_{\mathit{solvent}}+1}\right)\]

\[{c}_{\mathit{rf}}=\left(\frac{1}{{r}_{\mathit{cutoff}}}\right)\left(\frac{3{\epsilon}_{\mathit{solvent}}}{2{\epsilon}_{\mathit{solvent}}+1}\right)\]

where \(r_\mathit{cutoff}\) is the cutoff distance and \(\epsilon_\mathit{solvent}\) is the dielectric constant of the solvent. In the limit \(\epsilon_\mathit{solvent}\) >> 1, this causes the force to go to zero at the cutoff.

The reaction field approximation is not applied to nonbonded exceptions. They are always evaluated at full strength, regardless of the cutoff distance. That is because exceptions are primarily used to model 1-4 interactions, which are really a type of bonded interaction. They are typically parametrized without a cutoff together with the other bonded interactions.

19.6.4. Coulomb Interaction With Ewald Summation

For Ewald, the total Coulomb energy is the sum of three terms: the direct space sum, the reciprocal space sum, and the self-energy term.[46]

\[E=E_{\mathit{dir}}+{E}_{\mathit{rec}}+{E}_{\mathit{self}}\]

\[E_{\mathit{dir}}=\frac{1}{2}\sum _{i,j}\sum_\mathbf{n}{q}_{i}{q}_{j}\frac{\text{erfc}\left({\mathit{\alpha r}}_{ij,\mathbf{n}}\right)}{r_{ij,\mathbf{n}}}\]

\[E_{\mathit{rec}}=\frac{1}{2{\pi}V}\sum _{i,j}q_i q_j\sum _{\mathbf{k}{\neq}0}\frac{\text{exp}(-(\pi \mathbf{k}/\alpha)^2+2\pi i \mathbf{k} \cdot (\mathbf{r}_{i}-\mathbf{r}_{j}))}{\mathbf{m}^2}\]

\[E_{\mathit{self}}=-\frac{\alpha}{\sqrt{\pi}}\sum _{i}{q}_{{i}^{2}}\]

In the above expressions, the indices i and j run over all particles, n = (n₁, n₂, n₃) runs over all copies of the periodic cell, and k = (k₁, k₂, k₃) runs over all integer wave vectors from (-k_max, -k_max, -k_max) to (k_max, k_max, k_max) excluding (0, 0, 0). \(\mathbf{r}_i\) is the position of particle i , while \(r_{ij}\) is the distance between particles i and j. V is the volume of the periodic cell, and \(\alpha\) is an internal parameter.

In the direct space sum, all pairs that are further apart than the cutoff distance are ignored. Because the cutoff is required to be less than half the width of the periodic cell, the number of terms in this sum is never greater than the square of the number of particles.

The error made by applying the direct space cutoff depends on the magnitude of \(\text{erfc}({\alpha}r_\mathit{cutoff})\). Similarly, the error made in the reciprocal space sum by ignoring wave numbers beyond k_max depends on the magnitude of \(\text{exp}(-({\pi}k_{max}/{\alpha})^2\)). By changing \(\alpha\), one can decrease the error in either term while increasing the error in the other one.

Instead of having the user specify \(\alpha\) and -k_max, NonbondedForce instead asks the user to choose an error tolerance \(\delta\). It then calculates \(\alpha\) as

\[\alpha =\sqrt{-\text{log}\left(2{\delta}\right)}/{r}_{\mathit{cutoff}}\]

Finally, it estimates the error in the reciprocal space sum as

\[\mathit{error}=\frac{k_{\mathit{max}}\sqrt{d\alpha}}{20}\text{exp}(-(\pi k_\mathit{max}/d\alpha)^2)\]

where d is the width of the periodic box, and selects the smallest value for k_max which gives error < \(\delta\). (If the box is not square, k_max will have a different value along each axis.)

This means that the accuracy of the calculation is determined by \(\delta\). \(r_\mathit{cutoff}\) does not affect the accuracy of the result, but does affect the speed of the calculation by changing the relative costs of the direct space and reciprocal space sums. You therefore should test different cutoffs to find the value that gives best performance; this will in general vary both with the size of the system and with the Platform being used for the calculation. When the optimal cutoff is used for every simulation, the overall cost of evaluating the nonbonded forces scales as O(N^3/2) in the number of particles.

Be aware that the error tolerance \(\delta\) is not a rigorous upper bound on the errors. The formulas given above are empirically found to produce average relative errors in the forces that are less than or similar to \(\delta\) across a variety of systems and parameter values, but no guarantees are made. It is important to validate your own simulations, and identify parameter values that produce acceptable accuracy for each system.

19.6.5. Coulomb Interaction With Particle Mesh Ewald

The Particle Mesh Ewald (PME) algorithm[47] is similar to Ewald summation, but instead of calculating the reciprocal space sum directly, it first distributes the particle charges onto nodes of a rectangular mesh using 5th order B-splines. By using a Fast Fourier Transform, the sum can then be computed very quickly, giving performance that scales as O(N log N) in the number of particles (assuming the volume of the periodic box is proportional to the number of particles).

As with Ewald summation, the user specifies the direct space cutoff \(r_\mathit{cutoff}\) and error tolerance \(\delta\). NonbondedForce then selects \(\alpha\) as

\[\alpha =\sqrt{-\text{log}\left(2\delta\right)}/{r}_\mathit{cutoff}\]

and the number of nodes in the mesh along each dimension as

\[n_\mathit{mesh}=\frac{2\alpha d}{{3\delta}^{1/5}}\]

where d is the width of the periodic box along that dimension. Alternatively, the user may choose to explicitly set values for these parameters. (Note that some Platforms may choose to use a larger value of \(n_\mathit{mesh}\) than that given by this equation. For example, some FFT implementations require the mesh size to be a multiple of certain small prime numbers, so a Platform might round it up to the nearest permitted value. It is guaranteed that \(n_\mathit{mesh}\) will never be smaller than the value given above.)

The comments in the previous section regarding the interpretation of \(\delta\) for Ewald summation also apply to PME, but even more so. The behavior of the error for PME is more complicated than for simple Ewald summation, and while the above formulas will usually produce an average relative error in the forces less than or similar to \(\delta\), this is not a rigorous guarantee. PME is also more sensitive to numerical round-off error than Ewald summation. For Platforms that do calculations in single precision, making \(\delta\) too small (typically below about 5·10^-5) can actually cause the error to increase.

19.6.6. Lennard-Jones Interaction With Particle Mesh Ewald

The PME algorithm can also be used for Lennard-Jones interactions. Usually this is not necessary, since Lennard-Jones forces are short ranged, but there are situations (such as membrane simulations) where neglecting interactions beyond the cutoff can measurably affect results.

For computational efficiency, certain approximations are made[48]. Interactions beyond the cutoff distance include only the attractive \(1/r^6\) term, not the repulsive \(1/r^{12}\) term. Since the latter is much smaller than the former at long distances, this usually has negligible effect. Also, the interaction between particles farther apart than the cutoff distance is computed using geometric combination rules:

\[\sigma=\sqrt{\sigma_1 \sigma_2}\]

The effect of this approximation is also quite small, and it is still far more accurate than ignoring the interactions altogether (which is what would happen with PME).

The formula used to compute the number of nodes along each dimension of the mesh is slightly different from the one used for Coulomb interactions:

\[n_\mathit{mesh}=\frac{\alpha d}{{3\delta}^{1/5}}\]

As before, this is an empirical formula. It will usually produce an average relative error in the forces less than or similar to \(\delta\), but that is not guaranteed.

19.7. GBSAOBCForce

19.7.1. Generalized Born Term

GBSAOBCForce consists of two energy terms: a Generalized Born Approximation term to represent the electrostatic interaction between the solute and solvent, and a surface area term to represent the free energy cost of solvating a neutral molecule. The Generalized Born energy is given by[16]

\[E\text{=-}\frac{1}{2}\left(\frac{1}{\epsilon_{\mathit{solute}}}-\frac{1}{\epsilon_{\mathit{solvent}}}\right)\sum _{i,j}\frac{{q}_{i}{q}_{j}}{{f}_{\text{GB}}\left({d}_{ij},{R}_{i},{R}_{j}\right)}\]

where the indices i and j run over all particles, \(\epsilon_\mathit{solute}\) and \(\epsilon_\mathit{solvent}\) are the dielectric constants of the solute and solvent respectively, \(q_i\) is the charge of particle i, and \(d_{ij}\) is the distance between particles i and j. \(f_\text{GB}(d_{ij}, R_i, R_j)\) is defined as

\[{f}_{\text{GB}}\left({d}_{ij},{R}_{i},{R}_{j}\right)={\left[{d}_{ij}^2+{R}_{i}{R}_{j}\text{exp}\left(\frac{-{d}_{ij}^2}{{4R}_{i}{R}_{j}}\right)\right]}^{1/2}\]

\(R_i\) is the Born radius of particle i, which calculated as

\[R_i=\frac{1}{\rho_i^{-1}-r_i^{-1}\text{tanh}\left(\alpha \Psi_{i}-{\beta \Psi}_i^2+{\gamma \Psi}_i^3\right)}\]

where \(\alpha\), \(\beta\), and \(\gamma\) are the GB^OBCII parameters \(\alpha\) = 1, \(\beta\) = 0.8, and \(\gamma\) = 4.85. \(\rho_i\) is the adjusted atomic radius of particle i, which is calculated from the atomic radius \(r_i\) as \(\rho_i = r_i-0.009\) nm. \(\Psi_i\) is calculated as an integral over the van der Waals spheres of all particles outside particle i:

\[\Psi_i=\frac{\rho_i}{4\pi}\int_{\text{VDW}}\theta\left(|\mathbf{r}|-{\rho }_{i}\right)\frac{1}{{|\mathbf{r}|}^{4}}{d}^{3}\mathbf{r}\]

where \(\theta\)(r) is a step function that excludes the interior of particle i from the integral.

19.7.2. Surface Area Term

The surface area term is given by[49][50]

\[E=E_{SA} \cdot 4\pi \sum _{i}{\left({r}_{i}+{r}_{\mathit{solvent}}\right)}^{2}{\left(\frac{{r}_{i}}{{R}_{i}}\right)}^{6}\]

where \(r_i\) is the atomic radius of particle i, \(r_i\) is its atomic radius, and \(r_\mathit{solvent}\) is the solvent radius, which is taken to be 0.14 nm. The default value for the energy scale \(E_{SA}\) is 2.25936 kJ/mol/nm².

19.8. GayBerneForce

This is similar to the Lennard-Jones interaction described in section 19.6.1, but instead of being based on the distance between two point particles, it is based on the distance of closest approach between two ellipsoids.[51] Let \(\mathbf{A}_1\) and \(\mathbf{A}_2\) be rotation matrices that transform from the lab frame to the body frames of two interacting ellipsoids. These rotations are determined from the positions of other particles, as described in the API documentation. Let \(\mathbf{r}_{12}\) be the vector pointing from particle 1 to particle 2, and \(\hat{\mathbf{r}}_{12}=\mathbf{r}_{12}/|\mathbf{r}_{12}|\). Let \(\mathbf{S}_1\) and \(\mathbf{S}_2\) be diagonal matrices containing the three radii of each particle:

\[\begin{split}\mathbf{S}_i=\begin{bmatrix} a_i & 0 & 0 \\ 0 & b_i & 0 \\ 0 & 0 & c_i \end{bmatrix}\end{split}\]

The energy is computed as a product of three terms:

\[E=U_r(\mathbf{A}_1, \mathbf{A}_2, \mathbf{r}_{12}) \cdot \eta_{12}(\mathbf{A}_1, \mathbf{A}_2) \cdot \chi_{12}(\mathbf{A}_1, \mathbf{A}_2, \hat{\mathbf{r}}_{12})\]

The first term describes the distance dependence, and is very similar in form to the Lennard-Jones interaction:

\[U_r=4\epsilon\left({\left(\frac{\sigma}{h_{12}+\sigma}\right)}^{12}-{\left(\frac{\sigma}{h_{12}+\sigma}\right)}^{6}\right)\]

where \(h_{12}\) is an approximation to the distance of closest approach between the two ellipsoids:

\[h_{12}=|\mathbf{r}_{12}|-\sigma_{12}(\mathbf{A}_1, \mathbf{A}_2, \hat{\mathbf{r}}_{12})\]

\[\sigma_{12}(\mathbf{A}_1, \mathbf{A}_2, \hat{\mathbf{r}}_{12})=\left[ \frac{1}{2} \hat{\mathbf{r}}_{12}^T \mathbf{G}_{12}^{-1} \hat{\mathbf{r}}_{12} \right]^{-1/2}\]

\[\mathbf{G}_{12}=\mathbf{A}_1^T \mathbf{S}_1^2 \mathbf{A}_1 + \mathbf{A}_2^T \mathbf{S}_2^2 \mathbf{A}_2\]

The second term adjusts the energy based on the relative orientations of the two ellipsoids:

\[\eta_{12}(\mathbf{A}_1, \mathbf{A}_2)=\left[ \frac{2 s_1 s_2}{\text{det}(\mathbf{G}_{12})} \right]^{1/2}\]

\[s_i=(a_i b_i + c_i^2)\sqrt{a_i b_i}\]

The third term applies the user-defined scale factors \(e_a\), \(e_b\), and \(e_c\) that adjust the strength of the interaction along each axis:

\[\chi_{12}(\mathbf{A}_1, \mathbf{A}_2, \hat{\mathbf{r}}_{12})=(2 \hat{\mathbf{r}}_{12}^T \mathbf{B}_{12}^{-1} \hat{\mathbf{r}}_{12})^2\]

\[\mathbf{B}_{12}=\mathbf{A}_1^T \mathbf{E}_1 \mathbf{A}_1 + \mathbf{A}_2^T \mathbf{E}_2 \mathbf{A}_2\]

\[\begin{split}\mathbf{E}_i=\begin{bmatrix} e_{ai}^{-1/2} & 0 & 0 \\ 0 & e_{bi}^{-1/2} & 0 \\ 0 & 0 & e_{ci}^{-1/2} \end{bmatrix}\end{split}\]

When using a cutoff, you can optionally use a switching function to make the energy go smoothly to 0 at the cutoff distance. When \(r_\mathit{switch} < r < r_\mathit{cutoff}\), the energy is multiplied by

\[S=1-{6x}^{5}+15{x}^{4}-10{x}^{3}\]

where \(x = (r-r_\mathit{switch})/(r_\mathit{cutoff}-r_\mathit{switch})\). This function decreases smoothly from 1 at \(r = r_\mathit{switch}\) to 0 at \(r = r_\mathit{cutoff}\), and has continuous first and second derivatives at both ends.

19.9. AndersenThermostat

AndersenThermostat couples the system to a heat bath by randomly selecting a subset of particles at the start of each time step, then setting their velocities to new values chosen from a Boltzmann distribution. This represents the effect of random collisions between particles in the system and particles in the heat bath.[52]

The probability that a given particle will experience a collision in a given time step is

\[P=1-{e}^{-f\Delta t}\]

where f is the collision frequency and \(\Delta t\) is the step size. Each component of its velocity is then set to

\[{v}_{i}=\sqrt{\frac{{k}_{B}T}{m}}R\]

where T is the thermostat temperature, m is the particle mass, and R is a random number chosen from a normal distribution with mean of zero and variance of one.

19.10. MonteCarloBarostat

MonteCarloBarostat models the effect of constant pressure by allowing the size of the periodic box to vary with time.[53][54] At regular intervals, it attempts a Monte Carlo step by scaling the box vectors and the coordinates of each molecule’s center by a factor s. The scale factor s is chosen to change the volume of the periodic box from V to V+\(\Delta\)V:

\[s={\left(\frac{V+\Delta V}{V}\right)}^{1/3}\]

The change in volume is chosen randomly as

\[\Delta V=A\cdot r\]

where A is a scale factor and r is a random number uniformly distributed between -1 and 1. The step is accepted or rejected based on the weight function

\[\Delta W=\Delta E+P\Delta V-Nk_{B}T \text{ln}\left(\frac{V+\Delta V}{V}\right)\]

where \(\Delta E\) is the change in potential energy resulting from the step, P is the pressure being applied to the system, N is the number of molecules in the system, \(k_B\) is Boltzmann’s constant, and T is the system temperature. In particular, if \(\Delta W\le 0\) the step is always accepted. If \(\Delta W > 0\), the step is accepted with probability \(\text{exp}(-\Delta W/k_B T)\).

This algorithm tends to be more efficient than deterministic barostats such as the Berendsen or Parrinello-Rahman algorithms, since it does not require an expensive virial calculation at every time step. Each Monte Carlo step involves two energy evaluations, but this can be done much less often than every time step. It also does not require you to specify the compressibility of the system, which usually is not known in advance.

The scale factor A that determines the size of the steps is chosen automatically to produce an acceptance rate of approximately 50%. It is initially set to 1% of the periodic box volume. The acceptance rate is then monitored, and if it varies too much from 50% then A is modified accordingly.

Each Monte Carlo step modifies particle positions by scaling the centroid of each molecule, then applying the resulting displacement to each particle in the molecule. This ensures that each molecule is translated as a unit, so bond lengths and constrained distances are unaffected.

MonteCarloBarostat assumes the simulation is being run at constant temperature as well as pressure, and the simulation temperature affects the step acceptance probability. It does not itself perform temperature regulation, however. You must use another mechanism along with it to maintain the temperature, such as LangevinIntegrator or AndersenThermostat.

19.11. MonteCarloAnisotropicBarostat

MonteCarloAnisotropicBarostat is very similar to MonteCarloBarostat, but instead of scaling the entire periodic box uniformly, each Monte Carlo step scales only one axis of the box. This allows the box to change shape, and is useful for simulating anisotropic systems whose compressibility is different along different directions. It also allows a different pressure to be specified for each axis.

You can specify that the barostat should only be applied to certain axes of the box, keeping the other axes fixed. This is useful, for example, when doing constant surface area simulations of membranes.

19.12. MonteCarloMembraneBarostat

MonteCarloMembraneBarostat is very similar to MonteCarloBarostat, but it is specialized for simulations of membranes. It assumes the membrane lies in the XY plane. In addition to applying a uniform pressure to regulate the volume of the periodic box, it also applies a uniform surface tension to regulate the cross sectional area of the periodic box in the XY plane. The weight function for deciding whether to accept a step is

\[\Delta W=\Delta E+P\Delta V-S\Delta A-Nk_{B}T \text{ln}\left(\frac{V+\Delta V}{V}\right)\]

where S is the surface tension and \(\Delta\)A is the change in cross sectional area. Notice that pressure and surface tension are defined with opposite senses: a larger pressure tends to make the box smaller, but a larger surface tension tends to make the box larger.

MonteCarloMembraneBarostat offers some additional options to customize the behavior of the periodic box:

• The X and Y axes can be either

  • isotropic (they are always scaled by the same amount, so their ratio remains fixed)

  • anisotropic (they can change size independently)

• The Z axis can be either

  • free (its size changes independently of the X and Y axes)

  • fixed (its size does not change)

  • inversely varying with the X and Y axes (so the total box volume does not change)

19.13. MonteCarloFlexibleBarostat

MonteCarloFlexibleBarostat is very similar to MonteCarloBarostat, but it allows the periodic box to be fully flexible.[55] Monte Carlo steps can change not just the lengths of the box sides, but also the angles. It is especially useful for simulations of bulk materials where the shape of a crystal’s unit cell may not be known in advance, or could even change with time as it transitions between phases.

19.14. CMMotionRemover

CMMotionRemover prevents the system from drifting in space by periodically removing all center of mass motion. At the start of every n’th time step (where n is set by the user), it calculates the total center of mass velocity of the system:

\[\mathbf{v}_\text{CM}=\frac{\sum _{i}{m}_{i}\mathbf{v}_{i}}{\sum _{i}{m}_{i}}\]

where \(m_i\) and \(\mathbf{v}_i\) are the mass and velocity of particle i. It then subtracts \(\mathbf{v}_\text{CM}\) from the velocity of every particle.

19.15. RMSDForce

RMSDForce computes the root-mean-squared deviation (RMSD) between the current particle positions \(\mathbf{x}_i\) and a set of reference positions \(\mathbf{x}_i^\text{ref}\):

\[\text{RMSD} = \sqrt{\frac{\sum_{i} \| \mathbf{x}_i - \mathbf{x}_i^\text{ref} \|^2}{N}}\]

Before computing this, the reference positions are first translated and rotated so as to minimize the RMSD. The computed value is therefore \(argmin(\text{RMSD})\), where the \(argmin\) is taken over all possible translations and rotations.

This force is normally used with a CustomCVForce (see Section 20.11). One rarely wants a force whose energy exactly equals the RMSD, but there are many situations where it is useful to have a restraining or biasing force that depends on the RMSD in some way.