Harnessing deep learning for predicting nonlinear optical responses in 0D and 2D materials

The integration of deep learning with nonlinear optics opens new frontiers in understanding and predicting nonlinear optical responses of advanced 0D and 2D materials. Experimental data across diverse materials and deep learning frameworks correlate microscopic material properties with measured nonlinear responses. A paradigm of nonlinear optics combined with deep learning enables accurate computation of microscopic nonlinear responses from bulk material observables. A unified deep-structured learning architecture predicts the nonlinear optical response of 0D and 2D semiconductor materials at arbitrary wavelengths, capturing multiphoton processes up to the third order. Through model interpretation, insights on the impact of atomic features and common descriptors emerge, enabling accurate predictions based on a single lowest-order nonlinear response. Industry-wide applications accelerate research in nonlinear optics, quantum information, and related photonic fields. Nonlinear optics concerns phenomena where the optical response to an electromagnetic field depends on its intensity. Suspended 2D materials possess near-unity 100 GW/cm^2 |χ^(3)| nonlinearities across wavelengths from THz to visible and exhibit ballistic charge transport at room temperature through 20-nm channels. Individual colocalized dark-field scattering and surface-enhanced Raman scattering studies reveal strong electromagnetic field enhancement in sub-nm gaps between 2D heterostructures, with enhancement factors exceeding 10^7. Materials display transverse to longitudinal acoustic mode conversion dictated by incommensurability, along with intrinsic elastic energy dissipation up to 40 GHz. In deep-structured—or deep—learning, cascading of simple neural-network units compares the correctness of an input, such as text in translation-application software, against the guess of the output or ideal label that the network would like to obtain. Introducing a recursion operator at the network’s reasoning heart, a neural program interpreter learns from input-output pairs to induce simple algorithms.

Hali tarjima qilinmagan