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Investigation of arene and heteroarene nitration supported by high-throughput experimentation and machine learning

Year: 2025

Type: article

Abstract: A systematic study of nitration of (hetero)arenes was conducted using HTE. The generated data were used to develop a predictive tool relying on machine learning to evaluate the best reaction conditions depending on the targeted substrate.

Source: Digital Discovery

DOI: 10.1039/d5dd00086f

Authors: Taline Kerackian, Clément Wespiser, Matthieu Daniel, Eric Pasquinet, Eugénie Romero

Institutions: Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, CEA Le Ripault, Médicaments et Technologies pour la Santé

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4411574389

FWCI:

Cites: 59

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Related to: 10