Application of Bayesian Optimization and Mechanochemistry for the Evaluation of Challenging Substrates in Cross-Coupling Reactions

• Grant number: 25/26835-9
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: April 01, 2026
• End date: March 31, 2030
• Field of knowledge: Physical Sciences and Mathematics - Chemistry - Organic Chemistry
• Principal Investigator: Júlio Cezar Pastre
• Grantee: Jhully Anne Barros Carvalho Ribeiro
• Host Institution: Instituto de Química (IQ). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 21/06661-5 - Enabling technologies for the sustainable chemical synthesis of platform molecules, new chemicals and bioactive compounds, AP.BIOEN.JP2

Abstract

The efficient optimization of chemical reactions remains one of the major challenges in modern synthetic chemistry, especially when dealing with complex catalytic systems and substrates of low reactivity. In this context, Bayesian optimization (BO) has emerged as a data-driven strategy capable of accelerating experimental discovery by exploring large chemical spaces with a minimal number of trials. Mechanochemistry, in turn, offers a sustainable alternative to solution-phase synthesis, enabling solvent-free or solvent-reduced transformations with precise control of reaction parameters. This project aims to combine Bayesian optimization and mechanochemistry to develop nickel-catalyzed cross-coupling methodologies that are cleaner, faster, and more efficient. The study will focus on challenging substrates, such as aryl chlorides and heteroaryl chlorides, exploring three key reaction classes, Negishi coupling, Cross Electrophile Coupling (XEC), and Buchwald-Hartwig amination, each representing a research axis. Through iterative machine learning-guided optimization, the project aims to rationalize and expand the reactivity of nickel catalysts under milling conditions, reducing the experimental burden while uncovering new mechanistic insights. Developed under the supervision of Prof. Dr. Julio C. Pastre (UNICAMP) and Prof. Dr. Duncan L. Browne (UCL), this project builds upon expertise in mechanochemistry, consolidated throughout the master's research, and in Bayesian optimization, acquired during the BEPE-FAPESP internship at University College London. By integrating these two enabling tools, the project is expected to deliver cleaner and safer synthetic methodologies, broaden the understanding of nickel-catalyzed reactivity under mechanochemical conditions, and provide new insights into the mechanisms and limitations associated with challenging substrates. (AU)