Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Unlike common analytical techniques such as cyclic voltammetry, statistics‐based
optimization tools are not yet often in the toolbox of preparative organic electrochemists. In …

Although machine learning bears enormous potential to accelerate developments in
homogeneous catalysis, the frequent need for extensive experimental data can be a …

Owing to the unknown correlation of a metal's ligand and its resulting preferred speciation in
terms of oxidation state, geometry, and nuclearity, a rational design of multinuclear catalysts …

Inverse design allows the generation of molecules with desirable physical quantities using
property optimization. Deep generative models have recently been applied to tackle inverse …

Despite decades of effort, no earth-abundant homogeneous catalysts have been discovered
that can selectively oxidize methane to methanol. We exploit active learning to …

The design of molecules requires multi-objective optimizations in high-dimensional
chemical space with often conflicting target properties. To navigate this space, classical …

The challenge of activating inert C–H bonds motivates a study of catalysts that draws from
what can be accomplished by natural enzymes and translates these advantageous features …

Autonomous computations that rely on automated reaction network elucidation algorithms
may pave the way to make computational catalysis on a par with experimental research in …

The efficient exploration of chemical space to design molecules with intended properties
enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most …