  Herein we review aspects of leading-edge research and innovation in chemistry
which exploits big data and machine learning (ML), two computer science fields
that combine to yield machine intelligence. ML can accelerate the solution of
intricate chemical problems and even solve problems that otherwise would not be
tractable. But the potential benefits of ML come at the cost of big data
production; that is, the algorithms, in order to learn, demand large volumes of
data of various natures and from different sources, from materials properties
to sensor data. In the survey, we propose a roadmap for future developments,
with emphasis on materials discovery and chemical sensing, and within the
context of the Internet of Things (IoT), both prominent research fields for ML
in the context of big data. In addition to providing an overview of recent
advances, we elaborate upon the conceptual and practical limitations of big
data and ML applied to chemistry, outlining processes, discussing pitfalls, and
reviewing cases of success and failure.
