On the use of chemical bonding descriptors in machine learning

This review explores recent advances in machine learning in chemistry, emphasizing mechanistic understanding, performance optimization, and emerging design strategies. Key developments include novel synthesis routes, computational screening, hybrid experimental–theoretical approaches, and in-situ characterization. The review highlights how these innovations improve efficiency, selectivity, and scalability while uncovering fundamental structure-activity relationships. Special attention is given to integrating predictive modeling and high-throughput experimentation, which accelerates discovery cycles and enables rational design. Comparative discussions of different methodologies reveal synergies between traditional approaches and data-driven tools. Despite remarkable progress, translating laboratory results into practical applications remains a central challenge. The review concludes by outlining open questions, methodological gaps, and future research directions aimed at developing robust, cost-effective, and environmentally sustainable solutions.