Machine learning for laser-induced electron diffraction imaging of molecular structures
Diffraction imaging offers high spatiotemporal resolution, but fitting complex molecular structure data is expensive. Here, the authors use a machine learning algorithm with a convolutional neural network to retrieve a complex and large molecule, fenchone (C10H16O), from laser-induced electron diffr...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/d2b26194b0f3431e99966ab82846d27b |
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| Sumario: | Diffraction imaging offers high spatiotemporal resolution, but fitting complex molecular structure data is expensive. Here, the authors use a machine learning algorithm with a convolutional neural network to retrieve a complex and large molecule, fenchone (C10H16O), from laser-induced electron diffraction data without the need for fitting or ab initio calculations. |
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