Journal
PHYSICAL REVIEW LETTERS
Volume 125, Issue 20, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.125.206401
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Funding
- JSPS [20K19876]
- MEXT [19H05787, 19H00818]
- Grants-in-Aid for Scientific Research [19H05787, 19H00818, 20K19876] Funding Source: KAKEN
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Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn-Sham density functional theory (KS-DFT). Although this prediction is fast and accurate, we believe that a DNN model for KS-DFT must not only predict the properties but also provide the electron density of a molecule. This Letter presents the quantum deep field (QDF), which provides the electron density with an unsupervised but end-to-end physics-informed modeling by learning the atomization energy on a large-scale dataset. QDF performed well at atomization energy prediction, generated valid electron density, and demonstrated extrapolation.
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