  Symbolic regression plays a crucial role in modern scientific research thanks
to its capability of discovering concise and interpretable mathematical
expressions from data. A grand challenge lies in the arduous search for
parsimonious and generalizable mathematical formulas, in an infinite search
space, while intending to fit the training data. Existing algorithms have faced
a critical bottleneck of accuracy and efficiency over a decade when handling
problems of complexity, which essentially hinders the pace of applying symbolic
regression for scientific exploration across interdisciplinary domains. To this
end, we introduce a parallelized tree search (PTS) model to efficiently distill
generic mathematical expressions from limited data. Through a series of
extensive experiments, we demonstrate the superior accuracy and efficiency of
PTS for equation discovery, which greatly outperforms the state-of-the-art
baseline models on over 80 synthetic and experimental datasets (e.g., lifting
its performance by up to 99% accuracy improvement and one-order of magnitude
speed up). PTS represents a key advance in accurate and efficient data-driven
discovery of symbolic, interpretable models (e.g., underlying physical laws)
and marks a pivotal transition towards scalable symbolic learning.
