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 ch...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/27824 |
| Acceso en línea: | http://hdl.handle.net/10256/27824 |
| Access Level: | acceso abierto |
| Palabra clave: | Aprenentatge automàtic Machine learning Enllaços químics Chemical bonds Catàlisi Catalysis |
| Sumario: | 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 |
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