Instant prediction of relaxation in moiré superlattices using neural networks
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908542739415040 |
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| author | Belonovskii, Aleksei V. Girshova, Elizaveta I. Lähderanta, Erkki Kaliteevski, Mikhail |
| author_facet | Belonovskii, Aleksei V. Girshova, Elizaveta I. Lähderanta, Erkki Kaliteevski, Mikhail |
| contents | The relaxation of moiré superlattices in twisted bilayers of transition metal dichalcogenides (TMDs) has been modeled using a set of neural-network-based approaches. We implemented and compared several architectures, including (i) an interpolator combined with an autoencoder, (ii) an interpolator combined with a decoder, (iii) a direct generator mapping input parameters to displacement fields, and (iv) a physics-informed neural network (PINN). Among these, the direct generator architecture demonstrated the best performance, achieving machine-level precision with minimal training data. Remarkably, once trained, this simple fully connected network is able to predict the full displacement field of a moiré bilayer within a fraction of a second, whereas conventional continuum simulations require hours or even days. This finding highlights the low-dimensional nature of the relaxation process and establishes neural networks as a practical and efficient alternative to ab initio approaches for rapid modeling and high-throughput screening of 2D twisted heterostructures. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_13147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Instant prediction of relaxation in moiré superlattices using neural networks Belonovskii, Aleksei V. Girshova, Elizaveta I. Lähderanta, Erkki Kaliteevski, Mikhail Disordered Systems and Neural Networks The relaxation of moiré superlattices in twisted bilayers of transition metal dichalcogenides (TMDs) has been modeled using a set of neural-network-based approaches. We implemented and compared several architectures, including (i) an interpolator combined with an autoencoder, (ii) an interpolator combined with a decoder, (iii) a direct generator mapping input parameters to displacement fields, and (iv) a physics-informed neural network (PINN). Among these, the direct generator architecture demonstrated the best performance, achieving machine-level precision with minimal training data. Remarkably, once trained, this simple fully connected network is able to predict the full displacement field of a moiré bilayer within a fraction of a second, whereas conventional continuum simulations require hours or even days. This finding highlights the low-dimensional nature of the relaxation process and establishes neural networks as a practical and efficient alternative to ab initio approaches for rapid modeling and high-throughput screening of 2D twisted heterostructures. |
| title | Instant prediction of relaxation in moiré superlattices using neural networks |
| topic | Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2509.13147 |