  Stage IV dark energy surveys such as the Vera C. Rubin Observatory and Euclid
present a unique opportunity to shed light on the nature of dark energy.
However, the full constraining power of the new data cannot be unlocked unless
accurate predictions are available at all observable scales. Currently, only
the linear regime is well understood in models beyond $\Lambda$CDM: on the
nonlinear scales, expensive numerical simulations become necessary, making
their direct use impractical in the analyses of large datasets. Recently,
machine learning techniques have shown potential to break this impasse: by
training emulators, we can reproduce complex data fields in a fraction of the
time it takes to produce them.
  In this work, we present a field-level emulator capable of turning a
$\Lambda$CDM N-body simulation into one evolved under $f(R)$ gravity. To
achieve this, we build on the map2map neural network, using the strength of
modified gravity $|f_{R_0}|$ as style parameter. We find that our emulator
correctly estimates the changes it needs to apply to the positions and
velocities of the input N-body particles to produce the target simulation.
  We test the performance of our network against several summary statistics,
finding $1\%$ agreement in the power spectrum up to $k \sim 1$ $h/$Mpc, and
$1.5\%$ agreement against the independent boost emulator eMantis. Although the
algorithm is trained on fixed cosmological parameters, we find it can
extrapolate to models it was not trained on. Coupled with available field-level
emulators and simulations suites for $\Lambda$CDM, our algorithm can be used to
constrain modified gravity in the large-scale structure using full information
available at the field-level.
