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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Chemical Society
2023
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| 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 |