  Force-based algorithms for $\mathit{\text{ab initio}}$ atomic structure
relaxation, such as conjugate gradient methods, usually get stuck in the line
minimization processes along search directions, where expensive
$\mathit{\text{ab initio}}$ calculations are triggered frequently to test trial
positions before locating the next iterate. We present a force-based gradient
descent method, WANBB, that circumvents the deficiency. At each iteration,
WANBB enters the line minimization process with a trial stepsize capturing the
local curvature of the energy surface. The exit is controlled by an
unrestrictive criterion that tends to accept early trials. These two
ingredients streamline the line minimization process in WANBB. The numerical
simulations on nearly 80 systems with good universality demonstrate the
considerable compression of WANBB on the cost for the unaccepted trials
compared with conjugate gradient methods. We also observe across the board
significant and universal speedups as well as the superior robustness of WANBB
over several widely used methods. The latter point is theoretically
established. The implementation of WANBB is pretty simple, in that no a priori
physical knowledge is required and only two parameters are present without
tuning.
