This article presents an end-to-end functional bidirectional deep-learning (DL) model for three-dimensional chiral metamaterial design and optimization. This ML model utilizes multitask joint learning features to recognize, generalize, and explore in detail the nontrivial relationship between the metamaterials’ geometry and their chiroptical response, eliminating the need for auxiliary networks or equivalent approaches to stabilize the physically relevant output. This model efficiently realizes both forward and inverse retrieval tasks with great precision, offering a promising tool for iterative computational design tasks in complex physical systems. Other potential applications include photodetectors, polarization-resolved imaging, and circular dichroism (CD) spectroscopy.

This article combines in silico structural predictions with in planta complementation of the severely defective mtnip-1 mutant plants to understand the role of a series of distinct amino acids in the transporter’s function. The findings add to the knowledge of the mechanism of alternative conformational changes as well as symport transport in NPFs and enhance knowledge of the mechanisms for nitrate signaling.