The recent synergy of machine learning (ML) with molecular synthesis has emerged as an
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …

Recent years have witnessed a boom of machine learning (ML) applications in chemistry,
which reveals the potential of data‐driven prediction of synthesis performance. Digitalization …

Challenging enantio-and diastereoselective cobalt-catalyzed C–H alkylation has been
realized by an innovative data-driven knowledge transfer strategy. Harnessing the statistics …

Enantioselectivity prediction in asymmetric catalysis has been a long-standing challenge in
synthetic chemistry because of the high-dimensional nature of the structure …

The chemical sciences are witnessing an influx of statistics into the catalysis literature.
These developments are propelled by modern technological advancements that are leading …

We present the NaviCatGA package, a versatile genetic algorithm capable of optimizing
molecular catalyst structures using well‐suited fitness functions to achieve a set of targeted …

Feature representations, or descriptors, are machines' chemical language that largely
shapes the prediction capability, generalizability and interpretability of machine learning …

Cu-based materials are the most commonly used electrocatalysts for CO 2 reduction to
ethylene. The selectivity of copper-based catalysts is affected by many complicated and …

Catalytic asymmetric dearomatization (CADA) of phenols has emerged as a powerful
strategy for constructing stereochemically complicated architectures from planar aromatic …

In this account, we discuss the use of genetic algorithms in the inverse design process of
homogeneous catalysts for chemical transformations. We describe the main components of …