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Optical multilayer thin film structures are widely used in many photonic applica- tions, including filters, absorbers, photovoltaics, display devices. The important part to enable these applications is the inverse design, which seeks to identify a suitable structure that satisfy desired optical responses. Recently, a Foundation model-based OptoGPT is proposed and has shown great potential to solve a wide range of inverse design problems. However, OptoGPT fails to design certain types of optical responses that are important to practical applications. The major rea- son is that the training data is randomly sampled and it is highly probable that these design targets are not selected in training, leading to the out-of-distribution issue. In this work, we propose a self-improving data augmentation technique by leveraging neural networks’ extrapolation ability. Using this method, we show sig- nificant improvement in various application design tasks with minimum fine-tuning. The approach can be potentially generalized to other inverse scientific foundation models. 

High-quality and brilliant structural colors have been successfully produced using solution-based process over the past decade. 

Optical multilayer thin film structures have been widely used in numerous photonic applications. However, existing in- verse design methods have many drawbacks because they either fail to quickly adapt to different design targets, or are difficult to suit for different types of structures, e.g., designing for different materials at each layer. These methods also cannot accommodate versatile design situations under different angles and polarizations. In addition, how to benefit practical fabrications and manufacturing has not been extensively considered yet. In this work, we introduce OptoGPT (Opto Generative Pretrained Transformer), a decoder-only transformer, to solve all these drawbacks and issues simultaneously. 

Abstract Metal halide perovskites show promise for next-generation light-emitting diodes, particularly in the near-infrared range, where they outperform organic and quantum-dot counterparts. However, they still fall short of costly III-V semiconductor devices, which achieve external quantum efficiencies above 30% with high brightness. Among several factors, controlling grain growth and nanoscale morphology is crucial for further enhancing device performance. This study presents a grain engineering methodology that combines solvent engineering and heterostructure construction to improve light outcoupling efficiency and defect passivation. Solvent engineering enables precise control over grain size and distribution, increasing light outcoupling to ~40%. Constructing 2D/3D heterostructures with a conjugated cation reduces defect densities and accelerates radiative recombination. The resulting near-infrared perovskite light-emitting diodes achieve a peak external quantum efficiency of 31.4% and demonstrate a maximum brightness of 929 W sr⁻¹m⁻². These findings indicate that perovskite light-emitting diodes have potential as cost-effective, high-performance near-infrared light sources for practical applications.