  Solar flares - bursts of high-energy radiation responsible for severe
space-weather effects - are a consequence of the occasional destabilization of
magnetic fields rooted in active regions (ARs). The complexity of AR evolution
is a barrier to a comprehensive understanding of flaring processes and accurate
prediction. Though machine learning (ML) has been used to improve flare
predictions, the potential for revealing precursors and associated physics has
been underexploited. Here, we train ML algorithms to classify between
vector-magnetic-field observations from flaring ARs, producing at least one
M-/X-class flare, and non-flaring ARs. Analysis of magnetic-field observations
accurately classified by the machine presents statistical evidence for (1) ARs
persisting in flare-productive states --- characterized by AR area --- for
days, before and after M- and X-class flare events, (2) systematic pre-flare
build-up of free energy in the form of electric currents, suggesting that
associated subsurface magnetic field is twisted, (3) intensification of Maxwell
stresses in the corona above newly emerging ARs, days before first flares.
These results provide new insights into flare physics and improving flare
forecasting.
