HIGHLIGHTS
SUMMARY
The authors present such a method here, and show that the authors can accurately approximate a universal twist operator with a convolutional neural_network, with the overall concept outlined in Fig 1. Learning a generalized operator with neural_networks remains a hard problem even in recent years. Universal approximation theory gives some hope that certain operators can be learned by neural_networks, namely any nonlinear continuous operator. In Sec II, the authors introduce the electronic structure of these moiré 1D materials, the generation of the configuration-space LDOS maps, and the neural_network. A. 1D moiré electrons To . . .
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