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Reformulating Reactivity Design for Data-Efficient Machine Learning

[Image: see text] Machine learning (ML) can deliver rapid and accurate reaction barrier predictions for use in rational reactivity design. However, model training requires large data sets of typically thousands or tens of thousands of barriers that are very expensive to obtain computationally or exp...

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Detalles Bibliográficos
Autores principales: Lewis-Atwell, Toby, Beechey, Daniel, Şimşek, Özgür, Grayson, Matthew N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10594582/
https://www.ncbi.nlm.nih.gov/pubmed/37881791
http://dx.doi.org/10.1021/acscatal.3c02513