From the start of a synthetic chemist's training, experiments are conducted based on recipes
from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist …

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …

Applications of machine learning (ML) to synthetic chemistry rely on the assumption that
large numbers of literature-reported examples should enable construction of accurate and …

Assessing the outcomes of chemical reactions in a quantitative fashion has been a
cornerstone across all synthetic disciplines. Classically approached through empirical …

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 …

Nanoparticles play irreplaceable roles in optoelectronic sensing, medical therapy, material
science, and chemistry due to their unique properties. There are many synthetic pathways …

Ni/photoredox catalysis has emerged as a powerful platform for C (sp2)–C (sp3) bond
formation. While many of these methods typically employ aryl bromides as the C (sp2) …

High-throughput experimentation (HTE) is an increasingly important tool in reaction
discovery. While the hardware for running HTE in the chemical laboratory has evolved …

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 …

Conspectus In the domain of reaction development, one aims to obtain higher efficacies as
measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of …