In the molecular domain, numerous studies have explored the use of multimodal large language models (LLMs) to construct a general-purpose, multi-task molecular model. However, these efforts are still far from achieving a truly universal molecular model. We identify three key challenges in this endeavor: (1) Existing molecular task datasets are typically small in scale and lack comprehensive domain coverage. (2) Tasks from different molecular subfields are difficult to effectively learn jointly through LLMs due to significant distributional shifts and competition among tasks, which introduces instability in the learning process. (3) Both inter-task and intra-task molecular representations demand different intrinsic dimensions in the language space, making it challenging to balance between redundancy and insufficiency in language model representations. To address these challenges, we innovatively categorize existing small-molecule tasks into four types: Mol2Mol, Mol2Text, Mol2Num, and Text2Mol. We then collect a dataset encompassing over 16 tasks with more than 1.4 million samples, making it the largest molecular instruction-tuning dataset to date. Leveraging the extensive pretraining of LLMs on existing chemical literature, we propose a novel multimodal LLM framework, named **Omni-Mol**, which unifies all small-molecule tasks and supports both molecular generation and understanding. The core of Omni-Mol is our proposed MoGE, which dynamically adapts to the intrinsic rank of different tasks. This mixture-of-experts architecture enhances the model's ability to handle diverse tasks and modalities effectively. Our model achieves unified instruction tuning across 16 tasks and attains state-of-the-art performance on 13 of them. Extensive experiments further demonstrate the scalability and versatility of Omni-Mol.

Accurately obtaining the optical/microphysical characteristics of aerosols from satellite data is important for environmental protection and air quality monitoring. In this study, using Sentinel-2 satellite data, we propose a high-resolution satellite aerosol retrieval algorithm (CKAN) via convolutional neural network (CNN) and Kolmogorov-Arnold network (KAN). Unlike traditional retrieval algorithms that require the construction of physical models, the CKAN algorithm relies entirely on deep learning. This algorithm focuses on extracting high-dimensional information from the data through CNN and learning the potential nonlinear relationships between the data through the powerful fitting ability of KAN. Compared with the existing algorithms, the CKAN algorithm is characterized by simplicity and accuracy, and does not require a large amount of auxiliary meteorological data (e.g., relative humidity and ground air pressure) enables retrieval of various aerosol parameters, including Aerosol Optical Depth (AOD), Fine-mode AOD (FAOD), Coarse-mode AOD (CAOD), and Single Scattering Albedo (SSA). To demonstrate the effectiveness of the algorithm, we retrieved aerosol optical/microphysical characteristics from Sentinel-2 imagery for four study areas. Results indicate that both the AOD and FAOD retrieved by the CKAN algorithm exhibit a high degree of correlation ( R > 0.90) with AERONET products. For CAOD and SSA, although a few poor retrievals resulted in a low overall correlation, CKAN’s retrievals show good agreement with AERONET products. The CKAN algorithm has good accuracy in high-resolution satellite aerosol retrievals, and is expected to be widely used in urban-scale aerosol monitoring.