Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …

Machine learning (ML) has become a part of the fabric of high-throughput screening and
computational discovery of materials. Despite its increasingly central role, challenges …

Non-heme iron halogenases and hydroxylases activate inert C–H bonds to selectively
catalyze the functionalization of diverse biological products under physiological conditions …

Buchwald‐Hartwig amination reaction catalyzed by palladium plays an important role in
drug synthesis. In the last few years, machine learning‐assisted strategies emerged and …

The study aims to execute machine learning (ML) method for building an intelligent
prediction system for catalytic activities of a relatively big dataset of 1056 transition metal …

Access to six examples of α, α′-bis (imino)-2, 3: 5, 6-bis (pentamethylene) pyridine-iron (II)
chloride complex,[2, 3: 5, 6-{C4H8C (N (2-R1-4-R3-6-R2C6H2))} 2C5HN](R1= Me, R2= R3 …

In the areas of catalysis and organic chemistry, the development of versatile and efficient
catalytic systems has long been a challenge, primarily due to the intricate relationship …

Machine learning (ML), material genome, and big data approaches are highly overlapped in
their strategies, algorithms, and models. They can target various definitions, distributions …

We present a generic machine learning (ML) workflow for rapid screening of 3d-transition
metal pincer catalysts utilizing bis (imino) pyridine ligands for homogeneous polymerization …

The present study explored machine learning methods to predict the catalytic activities of a
dataset of 165 α-diimino nickel complexes in ethylene polymerization. Using 25 descriptors …