تقرير
Minimalist Neural Networks training for phase classification in diluted-Ising models
| العنوان: | Minimalist Neural Networks training for phase classification in diluted-Ising models |
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| المؤلفون: | Pavioni, G. L. Garcia, Arlego, M., Lamas, C. A. |
| المصدر: | Computational Materials Science Volume 235, 15 February 2024, 112792 |
| سنة النشر: | 2023 |
| المجموعة: | Condensed Matter |
| مصطلحات موضوعية: | Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Statistical Mechanics, Condensed Matter - Strongly Correlated Electrons |
| الوصف: | In this article, we explore the potential of artificial neural networks, which are trained using an exceptionally simplified catalog of ideal configurations encompassing both order and disorder. We explore the generalisation power of these networks to classify phases in complex models that are far from the simplified training context. As a paradigmatic case, we analyse the order-disorder transition of the diluted Ising model on several two-dimensional crystalline lattices, which does not have an exact solution and presents challenges for most of the available analytical and numerical techniques. Quantitative agreement is obtained in the determination of transition temperatures and percolation densities, with comparatively much more expensive methods. These findings highlight the potential of minimalist training in neural networks to describe complex phenomena and have implications beyond condensed matter physics. Comment: 9 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1016/j.commatsci.2024.112792 |
| URL الوصول: | http://arxiv.org/abs/2310.02122 |
| رقم الأكسشن: | edsarx.2310.02122 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1016/j.commatsci.2024.112792 |
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