Constraint algorithms
---------------------
Constraints can be imposed in |Gromacs| using LINCS (default) or the
traditional SHAKE method.
.. _shake:
SHAKE
~~~~~
The SHAKEÂ \ :ref:`46 <refRyckaert77>` algorithm changes a
set of unconstrained coordinates :math:`\mathbf{r}^{'}` to
a set of coordinates :math:`\mathbf{r}''` that fulfill a
list of distance constraints, using a set :math:`\mathbf{r}` reference, as
.. math:: {\rm SHAKE}(\mathbf{r}^{'} \rightarrow \mathbf{r}'';\, \mathbf{r})
:label: eqnshakebase
This action is consistent with solving a set of Lagrange multipliers in
the constrained equations of motion. SHAKE needs a *relative tolerance*;
it will continue until all constraints are satisfied within that
relative tolerance. An error message is given if SHAKE cannot reset the
coordinates because the deviation is too large, or if a given number of
iterations is surpassed.
Assume the equations of motion must fulfill :math:`K` holonomic
constraints, expressed as
.. math:: \sigma_k(\mathbf{r}_1 \ldots \mathbf{r}_N) = 0; \;\; k=1 \ldots K.
:label: eqnshakemotconstr
For example, :math:`(\mathbf{r}_1 - \mathbf{r}_2)^2 - b^2 = 0`.
Then the forces are defined as
.. math:: - \frac{\partial}{\partial \mathbf{r}_i} \left( V + \sum_{k=1}^K \lambda_k
\sigma_k \right),
:label: eqnshakeforce
where :math:`\lambda_k` are Lagrange multipliers which must be solved
to fulfill the constraint equations. The second part of this sum
determines the *constraint forces* :math:`\mathbf{G}_i`, defined by
.. math:: \mathbf{G}_i = -\sum_{k=1}^K \lambda_k \frac{\partial \sigma_k}{\partial
\mathbf{r}_i}
:label: eqnshakeconstrforces
The displacement due to the constraint forces in the leap-frog or
Verlet algorithm is equal to :math:`(\mathbf{G}_i/m_i)({{\Delta t}})^2`. Solving the
Lagrange multipliers (and hence the displacements) requires the solution
of a set of coupled equations of the second degree. These are solved
iteratively by SHAKE. :ref:`settle`
.. _settle:
SETTLE
~~~~~~
For the special case of rigid
water molecules, that often make up more than 80% of the simulation
system we have implemented the SETTLE algorithm \ :ref:`47 <refMiyamoto92>`
(sec. :ref:`constraintalg`). The implementation of SETTLE in |Gromacs|
is a slight modification of the original algorithm, in that it completely
avoids the calculation of the center of mass of the water molecule.
Apart from saving a few operations, the main gain of this is a reduction
in rouding errors. For large coordinates, the floating pointing precision of constrained
distances is reduced, which leads to an energy drift which usually depends
quadratically on the coordinate. For SETTLE this dependence is now linear, which enables
accurate integration of systems in single precision up to 1000 nm in size.
But note that the drift due to SHAKE and LINCS still has a quadratic
dependence, which limits the size of systems with normal constraints
in single precision to 100 to 200 nm.
For velocity Verlet, an additional round of constraining must be done,
to constrain the velocities of the second velocity half step, removing
any component of the velocity parallel to the bond vector. This step is
called RATTLE, and is covered in more detail in the original Andersen
paper \ :ref:`48 <refAndersen1983a>`.
LINCS
~~~~~
.. _lincs:
The LINCS algorithm
^^^^^^^^^^^^^^^^^^^
LINCS is an algorithm that resets bonds to their correct lengths after
an unconstrained update \ :ref:`49 <refHess97>`. The method is non-iterative,
as it always uses two steps. Although LINCS is based on matrices, no
matrix-matrix multiplications are needed. The method is more stable and
faster than SHAKE, but it can only be used with bond constraints and
isolated angle constraints, such as the proton angle in OH. Because of
its stability, LINCS is especially useful for Brownian dynamics. LINCS
has two parameters, which are explained in the subsection parameters.
The parallel version of LINCS, P-LINCS, is described in subsection
:ref:`plincs`.
The LINCS formulas
^^^^^^^^^^^^^^^^^^
We consider a system of :math:`N` particles, with positions given by a
:math:`3N` vector :math:`\mathbf{r}(t)`. For molecular
dynamics the equations of motion are given by Newton’s Law
.. math:: {{\mbox{d}}^2 \mathbf{r} \over {\mbox{d}}t^2} = {{\mathbf{M}}^{-1}}\mathbf{F},
:label: eqnc1
where :math:`\mathbf{F}` is the :math:`3N` force vector
and :math:`{\mathbf{M}}` is a :math:`3N \times 3N`
diagonal matrix, containing the masses of the particles. The system is
constrained by :math:`K` time-independent constraint equations
.. math:: g_i(\mathbf{r}) = | \mathbf{r}_{i_1}-\mathbf{r}_{i_2} | - d_i = 0 ~~~~~~i=1,\ldots,K.
:label: eqnc2
In a numerical integration scheme, LINCS is applied after an
unconstrained update, just like SHAKE. The algorithm works in two steps
(see figure :numref:`Fig. %s <fig-lincs>`). In the first step, the
projections of the new bonds on the old bonds are set to zero. In the
second step, a correction is applied for the lengthening of the bonds
due to rotation. The numerics for the first step and the second step are
very similar. A complete derivation of the algorithm can be found in
:ref:`49 <refHess97>`. Only a short description of the first step is given
here.
.. _fig-lincs:
.. figure:: plots/lincs.*
:height: 5.00000cm
The three position updates needed for one time step. The dashed
line is the old bond of length :math:`d`, the solid lines are the new
bonds. :math:`l=d \cos \theta` and :math:`p=(2 d^2 - l^2)^{1 \over 2}`.
A new notation is introduced for the gradient matrix of the constraint
equations which appears on the right hand side of this equation:
.. math:: B_{hi} = {{\partial}g_h \over {\partial}r_i}
:label: eqnc3
Notice that :math:`{\mathbf{B}}` is a :math:`K \times 3N`
matrix, it contains the directions of the constraints. The following
equation shows how the new constrained coordinates
:math:`\mathbf{r}_{n+1}` are related to the unconstrained
coordinates :math:`\mathbf{r}_{n+1}^{unc}` by
.. math:: \begin{array}{c}
\mathbf{r}_{n+1}=(\mathbf{I}-\mathbf{T}_n \mathbf{B}_n) \mathbf{r}_{n+1}^{unc} + {\mathbf{T}}_n \mathbf{d}=
\\[2mm]
\mathbf{r}_{n+1}^{unc} -
{{\mathbf{M}}^{-1}}\mathbf{B}_n ({\mathbf{B}}_n {{\mathbf{M}}^{-1}}{\mathbf{B}}_n^T)^{-1} ({\mathbf{B}}_n \mathbf{r}_{n+1}^{unc} - \mathbf{d})
\end{array}
:label: eqnm0
where
.. math:: {\mathbf{T}}= {{\mathbf{M}}^{-1}}{\mathbf{B}}^T ({\mathbf{B}}{{\mathbf{M}}^{-1}}{\mathbf{B}}^T)^{-1}
:label: eqnnm01
The derivation of this equation from :eq:`eqns. %s <eqnc1>` and
:eq:`%s <eqnc2>` can be found in :ref:`49 <refHess97>`.
This first step does not set the real bond lengths to the prescribed
lengths, but the projection of the new bonds onto the old directions of
the bonds. To correct for the rotation of bond :math:`i`, the projection
of the bond, :math:`p_i`, on the old direction is set to
.. math:: p_i=\sqrt{2 d_i^2 - l_i^2},
:label: eqnm1a
where :math:`l_i` is the bond length after the first projection. The
corrected positions are
.. math:: \mathbf{r}_{n+1}^*=(\mathbf{I}-\mathbf{T}_n \mathbf{B}_n)\mathbf{r}_{n+1} + {\mathbf{T}}_n \mathbf{p}.
:label: eqnm1b
This correction for rotational effects is actually an iterative
process, but during MD only one iteration is applied. The relative
constraint deviation after this procedure will be less than 0.0001 for
every constraint. In energy minimization, this might not be accurate
enough, so the number of iterations is equal to the order of the
expansion (see below).
Half of the CPU time goes to inverting the constraint coupling matrix
:math:`{\mathbf{B}}_n {{\mathbf{M}}^{-1}}{\mathbf{B}}_n^T`,
which has to be done every time step. This :math:`K \times K` matrix has
:math:`1/m_{i_1} + 1/m_{i_2}` on the diagonal. The off-diagonal elements
are only non-zero when two bonds are connected, then the element is
:math:`\cos \phi /m_c`, where :math:`m_c` is the mass of the atom
connecting the two bonds and :math:`\phi` is the angle between the
bonds.
The matrix :math:`\mathbf{T}` is inverted through a power
expansion. A :math:`K \times K` matrix :math:`\mathbf{S}`
is introduced which is the inverse square root of the diagonal of
:math:`\mathbf{B}_n {{\mathbf{M}}^{-1}}{\mathbf{B}}_n^T`.
This matrix is used to convert the diagonal elements of the coupling
matrix to one:
.. math:: \begin{array}{c}
({\mathbf{B}}_n {{\mathbf{M}}^{-1}}{\mathbf{B}}_n^T)^{-1}
= {\mathbf{S}}{\mathbf{S}}^{-1} ({\mathbf{B}}_n {{\mathbf{M}}^{-1}}{\mathbf{B}}_n^T)^{-1} {\mathbf{S}}^{-1} {\mathbf{S}}\\[2mm]
= {\mathbf{S}}({\mathbf{S}}{\mathbf{B}}_n {{\mathbf{M}}^{-1}}{\mathbf{B}}_n^T {\mathbf{S}})^{-1} {\mathbf{S}}=
{\mathbf{S}}(\mathbf{I} - \mathbf{A}_n)^{-1} {\mathbf{S}}\end{array}
:label: eqnm2
The matrix :math:`\mathbf{A}_n` is symmetric and sparse
and has zeros on the diagonal. Thus a simple trick can be used to
calculate the inverse:
.. math:: (\mathbf{I}-\mathbf{A}_n)^{-1}=
\mathbf{I} + \mathbf{A}_n + \mathbf{A}_n^2 + \mathbf{A}_n^3 + \ldots
:label: eqnm3
This inversion method is only valid if the absolute values of all the
eigenvalues of :math:`\mathbf{A}_n` are smaller than one.
In molecules with only bond constraints, the connectivity is so low that
this will always be true, even if ring structures are present. Problems
can arise in angle-constrained molecules. By constraining angles with
additional distance constraints, multiple small ring structures are
introduced. This gives a high connectivity, leading to large
eigenvalues. Therefore LINCS should NOT be used with coupled
angle-constraints.
For molecules with all bonds constrained the eigenvalues of :math:`A`
are around 0.4. This means that with each additional order in the
expansion :eq:`eqn. %s <eqnm3>` the deviations decrease by a factor 0.4. But for
relatively isolated triangles of constraints the largest eigenvalue is
around 0.7. Such triangles can occur when removing hydrogen angle
vibrations with an additional angle constraint in alcohol groups or when
constraining water molecules with LINCS, for instance with flexible
constraints. The constraints in such triangles converge twice as slow as
the other constraints. Therefore, starting with |Gromacs| 4, additional
terms are added to the expansion for such triangles
.. math:: (\mathbf{I}-\mathbf{A}_n)^{-1} \approx
\mathbf{I} + \mathbf{A}_n + \ldots + \mathbf{A}_n^{N_i} +
\left(\mathbf{A}^*_n + \ldots + {\mathbf{A}_n^*}^{N_i} \right) \mathbf{A}_n^{N_i}
:label: eqnm3ang
where :math:`N_i` is the normal order of the expansion and
:math:`\mathbf{A}^*` only contains the elements of
:math:`\mathbf{A}` that couple constraints within rigid
triangles, all other elements are zero. In this manner, the accuracy of
angle constraints comes close to that of the other constraints, while
the series of matrix vector multiplications required for determining the
expansion only needs to be extended for a few constraint couplings. This
procedure is described in the P-LINCS paper\ :ref:`50 <refHess2008a>`.
The LINCS Parameters
^^^^^^^^^^^^^^^^^^^^
The accuracy of LINCS depends on the number of matrices used in the
expansion :eq:`eqn. %s <eqnm3>`. For MD calculations a fourth order expansion is
enough. For Brownian dynamics with large time steps an eighth order
expansion may be necessary. The order is a parameter in the :ref:`mdp` file.
The implementation of LINCS is done in such a way that the algorithm
will never crash. Even when it is impossible to to reset the constraints
LINCS will generate a conformation which fulfills the constraints as
well as possible. However, LINCS will generate a warning when in one
step a bond rotates over more than a predefined angle. This angle is set
by the user in the :ref:`mdp` file.