Flow chemistry has unlocked a world of possibilities for the synthetic community, but the idea
that it is a mysterious “black box” needs to go. In this review, we show that several of the …

Machine learning models are increasingly being utilized to predict outcomes of organic
chemical reactions. A large amount of reaction data is used to train these models, which is in …

A closed-loop, autonomous molecular discovery platform driven by integrated machine
learning tools was developed to accelerate the design of molecules with desired properties …

Electrochemical research often requires stringent combinations of experimental parameters
that are demanding to manually locate. Recent advances in automated instrumentation and …

Out-of-equilibrium electrochemical reaction mechanisms are notoriously difficult to
characterize. However, such reactions are critical for a range of technological applications …

Data-driven materials science has realized a new paradigm by integrating materials domain
knowledge and machine-learning (ML) techniques. However, ML-based research has often …

Non-destructive, fast, and accurate methods of dating are highly desirable for many heritage
objects. Here, we present and critically evaluate the use of near-infrared (NIR) spectroscopic …

In a recent article in this journal, van Gerwen et al (2022 Mach. Learn.: Sci. Technol. 3
045005) presented a kernel ridge regression model to predict reaction barrier heights. Here …

Immobilization of cadmium (Cd) in soil is a complex systematic process in which biochar
materials and experimental conditions need to be selected from an infinite space of …

Carbon quantum dots (CQDs) have versatile applications in luminescence, whereas
identifying optimal synthesis conditions has been challenging due to numerous synthesis …