Energy Minimization#

Energy minimization in GROMACS can be done using steepest descent, conjugate gradients, or l-bfgs (limited-memory Broyden-Fletcher-Goldfarb-Shanno quasi-Newtonian minimizer…we prefer the abbreviation). EM is just an option of the mdrun program.

Steepest Descent#

Although steepest descent is certainly not the most efficient algorithm for searching, it is robust and easy to implement.

We define the vector \(\mathbf{r}\) as the vector of all \(3N\) coordinates. Initially a maximum displacement \(h_0\) (e.g. 0.01 nm) must be given.

First the forces \(\mathbf{F}\) and potential energy are calculated. New positions are calculated by

(120)#\[\mathbf{r}_{n+1} = \mathbf{r}_n + \frac{\mathbf{F}_n}{\max (|\mathbf{F}_n|)} h_n,\]

where \(h_n\) is the maximum displacement and \(\mathbf{F}_n\) is the force, or the negative gradient of the potential \(V\). The notation \(\max (|\mathbf{F}_n|)\) means the largest scalar force on any atom. The forces and energy are again computed for the new positions

If (\(V_{n+1} < V_n\)) the new positions are accepted and \(h_{n+1} = 1.2 h_n\).
If (\(V_{n+1} \geq V_n\)) the new positions are rejected and \(h_n = 0.2 h_n\).

The algorithm stops when either a user-specified number of force evaluations has been performed (e.g. 100), or when the maximum of the absolute values of the force (gradient) components is smaller than a specified value \(\epsilon\). Since force truncation produces some noise in the energy evaluation, the stopping criterion should not be made too tight to avoid endless iterations. A reasonable value for \(\epsilon\) can be estimated from the root mean square force \(f\) a harmonic oscillator would exhibit at a temperature \(T\). This value is

(121)#\[f = 2 \pi \nu \sqrt{ 2mkT},\]

where \(\nu\) is the oscillator frequency, \(m\) the (reduced) mass, and \(k\) Boltzmann’s constant. For a weak oscillator with a wave number of 100 cm\(^{-1}\) and a mass of 10 atomic units, at a temperature of 1 K, \(f=7.7\) kJ mol\(^{-1}\) nm\(^{-1}\). A value for \(\epsilon\) between 1 and 10 is acceptable.

Conjugate Gradient#

Conjugate gradient is slower than steepest descent in the early stages of the minimization, but becomes more efficient closer to the energy minimum. The parameters and stop criterion are the same as for steepest descent. In GROMACS conjugate gradient can not be used with constraints, including the SETTLE algorithm for water 47, as this has not been implemented. If water is present it must be of a flexible model, which can be specified in the mdp file by define = -DFLEXIBLE.

This is not really a restriction, since the accuracy of conjugate gradient is only required for minimization prior to a normal-mode analysis, which cannot be performed with constraints. For most other purposes steepest descent is efficient enough.


The original BFGS algorithm works by successively creating better approximations of the inverse Hessian matrix, and moving the system to the currently estimated minimum. The memory requirements for this are proportional to the square of the number of particles, so it is not practical for large systems like biomolecules. Instead, we use the L-BFGS algorithm of Nocedal  52, 53, which approximates the inverse Hessian by a fixed number of corrections from previous steps. This sliding-window technique is almost as efficient as the original method, but the memory requirements are much lower - proportional to the number of particles multiplied with the correction steps. In practice we have found it to converge faster than conjugate gradients, but due to the correction steps it is not yet parallelized. It is also noteworthy that switched or shifted interactions usually improve the convergence, since sharp cut-offs mean the potential function at the current coordinates is slightly different from the previous steps used to build the inverse Hessian approximation.