Quantum-chemical insights from deep tensor neural networks
Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in t...
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Autores principales: | Kristof T. Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R. Müller, Alexandre Tkatchenko |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2017
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Materias: | |
Acceso en línea: | https://doaj.org/article/459cc75532c24b7085302dabbc24454d |
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