  The Asteroid Terrestrial-impact Last Alert System (ATLAS) carries out its
primary planetary defense mission by surveying about 13000 deg^2 at least four
times per night. The resulting data set is useful for the discovery of variable
stars to a magnitude limit fainter than r~18, with amplitudes down to 0.01 mag
for bright objects. Here we present a Data Release One catalog of variable
stars based on analyzing 142 million stars measured at least 100 times in the
first two years of ATLAS operations. Using a Lomb-Scargle periodogram and other
variability metrics, we identify 4.7 million candidate variables which we
analyze in detail. Through Space Telescope Science Institute, we publicly
release lightcurves for all of them, together with a vector of 169
classification features for each star. We do this at the level of unconfirmed
candidate variables in order to provide the community with a large set of
homogeneously analyzed photometry and avoid pre-judging which types of objects
others may find most interesting. We use machine learning to classify the
candidates into fifteen different broad categories based on lightcurve
morphology. About 10% (430,000 stars) pass extensive tests designed to screen
out spurious variability detections: we label these as `probable' variables. Of
these, 230,000 receive specific classifications as eclipsing binaries,
pulsating, Mira-type, or sinusoidal variables: these are the `classified'
variables. New discoveries among the probable variables number more than
300,000, while 150,000 of the classified variables are new, including about
10,000 pulsating variables, 2,000 Mira stars, and 70,000 eclipsing binaries.
