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Exploring the configuration spaces of surface materials using time-dependent diffraction patterns and unsupervised learning
Computational methods for exploring the atomic configuration spaces of surface materials will lead to breakthroughs in nanotechnology and beyond. In order to develop such methods, especially ones utilizing machine learning approaches, descriptors which encode the structural features of the candidate...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Nature Publishing Group UK
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7125295/ https://www.ncbi.nlm.nih.gov/pubmed/32246027 http://dx.doi.org/10.1038/s41598-020-62782-6 |