Use of computational intelligence for the prediction of vacancy migration energies in atomistic kinetic monte carlo simulations
| Autor: | Lorenzo Malerba, Roberto P. Domingos, Nicolas Castin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2008 |
| Předmět: |
Work (thermodynamics)
Vacancy migration energy Theoretical computer science General Computer Science Computational complexity theory Artificial neural network Neural Networks Informatique générale Computer science Cluster Expansion Computational intelligence lcsh:QA75.5-76.95 Fuzzy logic Computational Mathematics Cluster expansion Fuzzy Logic Vacancy defect Dynamic Monte Carlo method Statistical physics Kinetic Monte Carlo lcsh:Electronic computers. Computer science Vacancy Migration Energy Biologie Neural networks |
| Zdroj: | International Journal of Computational Intelligence Systems, Vol 1, Iss 4 (2008) International Journal of Computational Intelligence Systems, 1 (4 International Journal of Computational Intelligence Systems, Vol 1, Iss 4, Pp 340-352 (2008) |
| ISSN: | 1875-6883 |
| Popis: | In this work, we try to build a regression tool to partially replace the use of CPU-time consuming atomic-level procedures for the calculation of point-defect migration energies in Atomistic Kinetic Monte Carlo (AKMC) simulations, as functions of the Local Atomic Configuration (LAC). Two approaches are considered: the Cluster Expansion (CE) and the Artificial Neural Network (ANN). The first is found to be unpromising because of its high computational complexity. On the contrary, the second provides very encouraging results and is found to be very well behaved. SCOPUS: ar.j info:eu-repo/semantics/published |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink