  The rapid development of machine learning (ML) methods has fundamentally
affected numerous applications ranging from computer vision, biology, and
medicine to accounting and text analytics. Until now, it was the availability
of large and often labeled data sets that enabled significant breakthroughs.
However, the adoption of these methods in classical physical disciplines has
been relatively slow, a tendency that can be traced to the intrinsic
differences between correlative approaches of purely data-based ML and the
causal hypothesis-driven nature of physical sciences. Furthermore, anomalous
behaviors of classical ML necessitate addressing issues such as explainability
and fairness of ML. We also note the sequence in which deep learning became
mainstream in different scientific disciplines - starting from medicine and
biology and then towards theoretical chemistry, and only after that, physics -
is rooted in the progressively more complex level of descriptors, constraints,
and causal structures available for incorporation in ML architectures. Here we
put forth that over the next decade, physics will become a new data, and this
will continue the transition from dot-coms and scientific computing concepts of
the 90ies to big data of 2000-2010 to deep learning of 2010-2020 to
physics-enabled scientific ML.
