As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Technologies such as batteries, biomaterials and heterogeneous catalysts have functions
that are defined by mixtures of molecular and mesoscale components. As yet, this multi …

Density functional theory (DFT) plays a pivotal role in chemical and materials science
because of its relatively high predictive power, applicability, versatility, and computational …

Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …

Discovering and optimizing commercially viable materials for clean energy applications
typically takes more than a decade. Self-driving laboratories that iteratively design, execute …

Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring
chemical space more widely and efficiently. Chemical space is monumentally large, but …

Computing is a critical driving force in the development of human civilization. In recent years,
we have witnessed the emergence of intelligent computing, a new computing paradigm that …

This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …

Consumer analytics is at the epicenter of a Big Data revolution. Technology helps capture
rich and plentiful data on consumer phenomena in real time. Thus, unprecedented volume …