Recent advances of machine learning applications in the development of experimental homogeneous catalysis
Machine Learning (ML) stands as a disruptive technology, finding application across a diverse array of scientific disciplines. When applied to homogeneous catalysis, this technology accelerates catalyst discovery through virtual screening, which not only reduces experimental iterations but also yiel...
| Autores: | , , , , |
|---|---|
| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/387600 |
| Acesso em linha: | http://hdl.handle.net/10261/387600 |
| Access Level: | acceso abierto |
| Palavra-chave: | Machine learning Homogeneous catalysis DFT Calculations Cheminformatics Predictive models |
| Resumo: | Machine Learning (ML) stands as a disruptive technology, finding application across a diverse array of scientific disciplines. When applied to homogeneous catalysis, this technology accelerates catalyst discovery through virtual screening, which not only reduces experimental iterations but also yields significant savings in time, resources, and waste generation. ML algorithms, often integrated with cheminformatic tools and quantum mechanics featurization, excel in predicting reaction outcomes that guide the engineering of catalysts for desired reactivity and selectivity. This minireview presents recent studies regarding databases as well as supervised and unsupervised problems, offering a general yet insightful perspective on the current ML-driven progress in homogeneous catalysis. |
|---|