| Autor: |
Behnam Parsaeifard, Deb Sankar De, Jonas A. Finkler, Stefan Goedecker |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Condensed Matter, Vol 6, Iss 1, p 9 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2410-3896 |
| DOI: |
10.3390/condmat6010009 |
| Popis: |
Using fingerprints used mainly in machine learning schemes of the potential energy surface, we detect in a fully algorithmic way long range effects on local physical properties in a simple covalent system of carbon atoms. The fact that these long range effects exist for many configurations implies that atomistic simulation methods, such as force fields or modern machine learning schemes, that are based on locality assumptions, are limited in accuracy. We show that the basic driving mechanism for the long range effects is charge transfer. If the charge transfer is known, locality can be recovered for certain quantities such as the band structure energy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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