Instant Prediction of Moire Superlattice Relaxation in Twisted Bilayers of Transition Metal Dichalcogenides Using Different Neural Network Architectures
Year: 2025
Type: article
Abstract: The relaxation of moiré superlattices in twisted bilayers of transition metal dichalcogenides (TMDs) has been modeled using a set of neural-network (NN)-based approaches. The physical system considered is described by a small number of parameters (layer properties, twist angle, type of solution (straight or twirl)); thus, the number of input parameters for the NN should be small. Principal Component Analysis demonstrates that 99.99% of the information describing the structure is provided by 7 parameters. We implemented and compared several NN 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.
Institution: Lappeenranta-Lahti University of Technology
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Funder: Suomen Kulttuurirahasto
Award: G2393176626
OpenAlex ID: W4417466201