Locality meets machine learning: Excited and ground-state energy surfaces of large systems at the cost of small ones
| Autor: | Yavar T. Azar, Ali Sadeghi, Mahboobeh Babaei |
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| Rok vydání: | 2020 |
| Předmět: |
Scheme (programming language)
Computer science business.industry Locality Energy landscape 02 engineering and technology Electronic structure 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences Excited state 0103 physical sciences Artificial intelligence 010306 general physics 0210 nano-technology Ground state business computer Large model computer.programming_language |
| Zdroj: | Physical Review B. 101 |
| ISSN: | 2469-9969 2469-9950 |
| DOI: | 10.1103/physrevb.101.115132 |
| Popis: | It is demonstrated that supervised machine learning of local environments of the atoms in a molecule can be very effectively combined with the density functional-based tight-binding method to provide a fast and size-extensive scheme for electronic structure calculations. We train our machine learning model on small and basic molecules and then run it successfully for large and complicated molecules. This facilitates investigations of structural, electronic, and optical properties of large model systems for which the conventional iterative self-consistent procedure becomes too costly. The fruitfulness of this shortsightedness-based scheme is shown for describing the energy landscape of the ground and low-lying excited states of several model molecules of variant sizes and complexity. The achieved accuracy in the tests supports the locality view to the electronic redistribution in a molecule and promises the efficiency of the machine learning equipped divide-and-conquer approach for solving the Schr\"odinger equation. |
| Databáze: | OpenAIRE |
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